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NeuroRehabilitation 34 (2014) 355–363 DOI:10.3233/NRE-131039 IOS Press
Effect of real-time cortical feedback in motor imagery-based mental practice training Ou Baia,∗ , Dandan Huanga , Ding-Yu Feia and Richard Kunzb a EEG
& BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA b Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, USA
Abstract. BACKGROUND: Mental practice using motor imagery of limb movement may facilitate motor recovery in persons who have experienced cerebrovascular accident (CVA). However, the lack of a feedback mechanism that can monitor the quality of the motor imagery affects patients’ engagement and motivation to participate in the mental practice training program. OBJECTIVE: This study investigates the effect of novel real-time motor imagery-associated cortical activity feedback on motor imagery-based mental practice training. METHODS: Ten healthy volunteers were randomly assigned into intervention and control groups. Both groups participated in a five-visit motor imagery-based mental practice training program managed over a period of two months. The intervention group received mental practice training with real-time feedback of movement-associated cortical activity—beta band (16–28 Hz) event-related desynchronization (ERD) in electroencephalography (EEG), using a novel custom-made brain-computer interface (BCI) system. The control group received the mental practice training program without EEG cortical feedback. Motor excitability was assessed by measuring the frequency power magnitude of the EEG rhythmic activity associated with physical execution of wrist extension before and after the motor imagery-based mental practice training. RESULTS: The EEG frequency power magnitude associated with the physical execution of wrist extension was significantly lower (i.e. more desynchronized) after the mental practice training in the intervention group that received real-time cortical feedback (P < 0.05), whereas no significant difference in EEG frequency power magnitude associated with the physical execution of wrist extension was observed before and after mental practice training in the control group who did not receive feedback. CONCLUSIONS: The mental practice training program with motor imagery-associated cortical feedback facilitated motor excitability during the production of voluntary motor control. Motor imagery-based mental practice training with movementassociated cortical activity feedback may provide an effective strategy to facilitate motor recovery in brain injury patients, particularly during the early rehabilitation stage when full participation in physical and occupational therapy programs may not be possible due to excessive motor weakness. Keywords: Motor imagery, mental practice, motor control, electroencephalography (EEG), brain-computer interface (BCI), eventrelated desynchronization (ERD)
1. Introduction
∗ Address
for correspondence: Ou Bai, Ph.D., Director of EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, 401 West Main Street, Room 1252, Richmond, VA 23284-3067, P.O. Box 843067, USA. Tel.: +1 804 827 3607; Fax: +1 804 828 4454; E-mail: [email protected].
With the aging population and improved survival after initial injury, the prevalence and incidence of individuals living with disability following cerebral vascular accident (CVA) has increased (Volpe et al., 2009). These individuals will learn or relearn competencies necessary to perform activities of daily living
1053-8135/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved
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(ADL). Traditionally, the practice of skills provided in neurologic rehabilitation has focused on reducing motor impairment and minimizing physical disability (Langhorne, 2004; Poole, 1991). New rehabilitative approaches emphasize repetitive, task-specific practice with the affected arm, such as the contralateral limb restraint practice (Azab et al., 2009; Brogardh & Sjolund, 2006). Other novel methods to facilitate motor recovery use either an externally generated magnetic field targeting the brain (e.g. transcranial magnetic stimulation) or an externally generated electrical stimulus (e.g. functional or neuromuscular electrical stimulation) targeting the muscles of the upper and lower limbs (Hallett, 2000). Mirror therapy, in which the unaffected limb is presented to the individual as the affected limb using a reflection in the midsagittal plane, has also been employed as a rehabilitation tool (Dohle et al., 2009). These emerging methods aim to restore motor control through a change in brain function, i.e. motor relearning (Bonifer, Anderson, & Arciniegas, 2005; Wolf et al., 2008). Although promising, there are a large proportion of persons who have experienced a CVA for whom none of these methods has restored functional motor control. It is important to explore new methods that may facilitate recovery of brain function and the restoration of more normal motor control. Recently, an emerging method has been explored that employs mental practice using motor imagery of limb movement to facilitate motor recovery in persons who have experienced a CVA. This technique consists of training patients to rehearse a dynamic mental image of the desired motor output, typically in a hemiparetic limb. The mental motor imagery of limb movement is a complex cognitive operation involving self-generated intent using sensory and perceptual processes with outcomes of specific motor actions within working memory (Annett, 1995; Jeannerod & Decety, 1995; Kosslyn, Ganis, & Thompson, 2001; Michelon, Vettel, & Zacks, 2006). Scientific rationale within motor imagery-based mental training is established on: (1) the motor imagery incorporates a voluntary drive (Kimberley et al., 2006), an essential factor for motor learning after neurological injury (Lotze, Braun, Birbaumer, Anders, & Cohen, 2003); and (2) physically executed and imagined movements share a common neural substrate. It follows that the motor imagery-based mental practice training may also facilitate motor recovery similar to the physical training (Facchini, Muellbacher, Battaglia, Boroojerdi, & Hallett, 2002; Rossini, Rossi, Pasqualetti, & Tecchio, 1999; Sharma, Pomeroy, & Baron, 2006). Motor
imagery mental practice may contribute to the activation of neural descending and ascending loops of movement pathways which are represented in the brain with a kind of motor print (Butler & Page, 2006). There is also evidence of neural reorganization as a result of motor imagery mental practice training (Lotze & Cohen, 2006; Lotze & Halsband, 2006). Motor imagery-based mental practice might be incorporated into multidisciplinary rehabilitation programs as an adjunct to physical and occupational therapy programs for individuals recovering from CVA. Such adjunctive programs will probably be most effective during the early stage of recovery when neural plasticity and reorganization is most prominent (Sharma et al., 2006) and when motor execution training is still not fully possible due to patients’ excessive muscle weakness (Lotze & Cohen, 2006). There is evidence demonstrating the efficacy of such motor imagery-based mental practice training strategies. A case study reported on an individual patient with upper-limb hemiparesis who received a program combining physical therapy for the affected arm with mental practice during the sub-acute phase of recovery. The mental practice training consisted of listening to an audiotape instructing them to imagine functionally using their affected limb (Page, Levine, Sisto, & Johnston, 2001). After 6 weeks training, the patient exhibited reduction in impairment measured by the Fugl-Meyer scale (Fugl-Meyer, Jaasko, Leyman, Olsson, & Steglind, 1975) and improvement in arm function measured by the Action Research Arm Test (ARAT) (Lyle, 1981), and the Stroke Rehabilitation Assessment of Movement (STREAM) (Daley, Mayo, & Wood-Dauphinee, 1999). The investigators suggest that “mental practice may complement physical therapy to improve motor function after stroke.” Smania and colleagues demonstrated that visuomotor imagery training was effective “in diminishing impairment and functional disability associated with contralateral neglect” (Smania, Bazoli, Piva, & Guidetti, 1997). Randomized, controlled trials have shown that individuals in the chronic phase (greater than six weeks after injury) of recovery following stroke who mentally rehearsed functional activities of daily living showed significant improvement of Functional Independence Measure (FIM) and ARAT scores compared with individuals who received physical training only (Liu, Chan, Lee, & Hui-Chan, 2004; Page, Levine, & Leonard, 2005). Additionally, Niemeier’s group (Niemeier, 1998; Niemeier, Cifu, & Kishore, 2001) as well as Bailey’s group (Bailey, Riddoch, & Crome,
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2002) have demonstrated that mental training using a “Lighthouse Strategy” of turning the head to scan left and right in order to address visual neglect significantly improved functional capabilities in route finding and problem solving among persons who have experienced CVA. The critical barrier to achieving efficient motor imagery-based mental practice training for motor recovery is the lack of a feedback mechanism that can monitor the quality/performance of the motor imagery. This may affect patients’ engagement and motivation to participate in the mental practice training program. Both the clinician and individual recovering from CVA will benefit from knowing whether – and how effectively – they are engaged in the motor imagery-based mental practice. There are several objective measures available to provide such validation including dynamic brain imaging studies, autonomic monitoring, and mental chronometry. Dynamic brain imaging studies using fMRI have confirmed the neural substrates associated with the motor imagery mental practice as well as the neural reorganization that results from such mental practice (Berman, Horovitz, Venkataraman, & Hallett, 2012; Hanakawa, Dimyan, & Hallett, 2008; Kimberley et al., 2006; Sharma et al., 2009). However, fMRI is not practical as a real-time tool for monitoring the quality of motor imagery mental practice due to its long time-constant as well as the costly magnetic resonance equipment. Autonomic nervous system (ANS) activity recordings have been proposed for assessing motor imagery-based mental practice effort based upon that increased heart and respiratory rate may indicate an increase in mental effort (Guillot & Collet, 2005). However, such an analysis provides only an indirect assessment of central nervous system activity. In addition, because the aim of motor imagery-based mental practice is to activate motor networks, it is crucial that patients perform motor imagery of the self-generated movement tasks as opposed to non-movement related mental imagery (Sharma et al., 2006). However, movement and non-movement associated mental imagery tasks modulate similar ANS activities and may be indistinguishable with ANS monitoring. Mental chronometry for mental training is based on the observation that the duration of mentally simulated and physically executed motor tasks are comparable as the subjects produce mental images of motor tasks following an external metronome. There are some critical limitations to mental chronometry (Dickstein & Deutsch, 2007). First, it does not provide information about the vividness of motor imagery but
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rather the characteristic of time perseveration (Sirigu et al., 1995). Second, subjects may tend to count time instead of focusing on the motor imagery tasks (Sharma et al., 2006). Hence, the currently available technologies for monitoring/assessing motor imagery-based mental practice training remain insufficient. Human voluntary movement is associated with changes in cortical oscillations, which can be seen in scalp electroencephalography (EEG) and magnetoencephalography (MEG). Pfurtscheller and colleagues reported a sustained block or decrease of rhythmic oscillations, termed as event-related desynchronization (ERD) in alpha band (8–12 Hz) (Pfurtscheller & Aranibar, 1979) and in central beta-band (16–28 Hz) (Pfurtscheller, 1981) before and during the production of self-paced voluntary movements. ERD occurs not only associated with physical execution of voluntary movement, but also during the production of motor imagery of kinematic movements, i.e. subjects merely imagining their own body movement without actual physical execution (Huang, Lin, Fei, Chen, & Bai, 2009; McFarland, Miner, Vaughan, & Wolpaw, 2000; Pfurtscheller, Neuper, Brunner, & da Silva, 2005). Recently, a series of studies have reported the design of novel brain-computer interface (BCI) systems which allow subjects to drive a computer cursor to navigate a virtual world by performing motor imagery alone without actual physical movement (Guan, 2009; Huang et al., 2009; Pfurtscheller et al., 2006). Pfurtscheller and colleagues have also successfully applied the motor imagery strategy to control a functional electrical stimulation (FES) device to restore hand grasp in a patient with tetraplegia (Pfurtscheller, Muller, Pfurtscheller, Gerner, & Rupp, 2003). On one hand, these applications of motor imagery training create the possibility of driving assistive devices for the augmentation of motor function. On the other hand, these successful virtual controls via motor imagery also indicate that an EEG signal may provide a suitable indication to monitor the accuracy and quality of the motor imagery. In the present study, we have developed a novel brain-computer interface (BCI) system for online EEG activity feedback that provides a real-time indication of the motor imagery quality for mental practice training. The objective of this study is to determine the effect of real-time motor imagery-associated cortical feedback on the motor imagery-based mental practice training program. We hypothesized that the corticomotoneuronal motor excitability during the production of voluntary movement could be more efficiently promoted by the motor imagery-based mental practice
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training with cortical activity feedback than that without cortical feedback.
2. Methods 2.1. Subjects and group assignment Ten healthy volunteers (eight males and two females; age 20.8 ± 0.7 years old) participated in the study. Nine were right handed and one was left handed according to the Edinburgh inventory (Oldfield, 1971). None of the subjects had prior experience of similar feedback training. The protocol was approved by the Institutional Review Board, and all subjects gave their written informed consent for the study. Subjects were randomly assigned into the intervention and control groups. The assignment was even for the two groups. The subjects in the intervention group received motor imagery-based mental practice training with cortical activity feedback via EEG; the subjects in the control group received motor imagery-based mental practice training only, without cortical activity feedback.
including experiment setup (15–20 min) and six practice sessions was approximately 1.5 hours per visit. Subjects in both the intervention and control groups were asked to perform repeated motor imagery of kinematic hand movement following an audio cue provided by the custom-made cortical activity feedback system. At the first visit before the mental practice training program began, subjects in both groups were instructed to create a mental image in 1st-person perspective of alternating wrist extension from the neutral position, imagining also the sensations associated with muscle extension in their forearm. During the mental training, subjects initiated motor imagery of this wrist extension following a high pitch tone (9000 Hz, duration = 0.05 s), imagining that they were extending their wrist fully and sustaining the extended position for 2 seconds until they were presented with a low pitch tone (3000 Hz, duration = 0.05 s). Then subjects were instructed to maintain a mental image of the wrist relaxed in the neutral position for 2.5 seconds until the next high pitch tone was presented to repeat the cycle. Subjects repeated the same motor task 50 times during a single session.
2.2. Experimental and training protocol
2.3. Brain-computer interface system for cortical feedback
Subjects were seated in a chair with the tested forearm supported by a pillow. They were asked to alternately initiate and cease a motor imagery task of wrist movement (detailed description in the following paragraphs) according to auditory cues provided by a custom-made feedback system. Subjects were instructed to keep all muscles relaxed throughout the mental practice training sessions. In particular, when performing motor imagery, subjects were asked not to move their hand. One of the investigators monitored them visually to confirm no physical hand movement. Subjects were instructed to avoid ocular movements, eye blinking, postural adjustments, throat clearing, and other body or limb movements. The training program consisted of five separate visits, which were managed over a period of 2 months. There were 1–2 weeks between two consecutive visits according to subjects’ availability for the study. At each visit, the experimental procedure consisted of six motor imagery-based mental practice sessions; three sessions of right hand kinematic motor imagery and three sessions of left hand kinematic motor imagery. Each session lasted about 5–6 min with a 2–3 min break between two consecutive sessions. The total duration
A novel custom-made brain-computer interface (BCI) system was developed for EEG acquisition and processing to provide “online” cortical activity feedback in real-time. The diagram of the BCI-based feedback system is illustrated in Fig. 1. EEG was read from 4 (tin) surface electrodes (C3, C1, C2, C4) situated over motor cortex according to the international 10-20 system (Jasper & Andrews, 1938), which were mounted on an elastic cap (Electro-Cap International, Inc., Eaton, OH, USA). The four-channel EEG signals were referenced to a single electrode of Fz. The distance between two adjacent electrodes was approximately 2.5 cm. EEG signals from all channels were amplified, filtered (DC-100 Hz), and digitized (sampling frequency, 256 Hz) in a commercial EEG amplifier (g.tec GmgH, Schiedlberg, Austria). The EEG digital signals were then sent to a Hewlett Packard PC workstation equipped with a 2.33 GHz Xeon CPU and were processed in real time using a home-made MATLAB (MathWorks, Natick, MA) Toolbox: brain-computer interface to virtual reality or BCI2VR (Bai et al., 2007, 2008). The frequency power for ERD estimation was calculated online in realtime by self-made MATLAB scripts embedded in the
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Fig. 1. Schematic diagram of the BCI-based cortical feedback system for motor imagery-based mental practice training. Four EEG channels placed over motor cortex areas were amplified and digitized in a commercial EEG amplifier and then the computer converted the EEG signal into frequency power. The beta band ERD for the four EEG channels was extracted and fed back to the participants of the intervention group on a computer monitor in real-time. The participants were asked to create vivid kinematic mental motor imagery of their right and left wrist movement such that the blue bars on the monitor were lower than the red line, i.e. a higher ERD.
Fig. 2. Paired-t tests of EEG rhythmic frequency power magnitudes associated with the physical execution of right wrist extension. For the intervention group, the EEG rhythmic frequency power magnitude was significantly lower (P < 0.05) after the motor imagery-based mental practice training with cortical feedback compared to pre-training measurement. There was no significant before-after difference of the EEG rhythmic frequency power magnitude in the control group that received training without cortical feedback.
BCI2VR. The Welch method with a Hamming window was employed for the power spectral density (PSD) estimation in order to reduce the estimation variance and side-lobe effect (Welch, 1967); single-trial data in the selected time-window was segmented and periodograms from all segments were averaged to obtain a smoothed estimation. A 4 Hz frequency resolution, or
segment length of 256/4 = 64 under 50% overlapping, was used in Welch-based PSD estimation. Beta band power from 16–28 Hz was extracted for feedback. The beta power was converted to decibel scales. ERD was the difference between the decibel power in the ERD window and baseline power; a higher amplitude of ERD indicates a smaller amplitude of rhythmic activity in
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the ERD windows compared with the baseline activity. The estimated ERD amplitudes of the four EEG channels over motor areas were then fed back to the subject on a computer monitor that was situated approximately 1.5 m before the subject. The cortical ERD feedback system also saved the online ERD data for offline analysis. As shown in Fig. 1, the cortical ERD activity of the four EEG channels was fed back to the subjects on a computer monitor by four column panels. When a low pitch tone was delivered, the subjects ceased motor imagery and the ERD was provided on the monitor immediately. The blue bars represent real-time ERD amplitudes estimated from the EEG activity associated with the motor imagery performed. The red bars represent a preset target value for ERD, i.e. 50% of the ERD associated with physical wrist extension obtained in the first visit. Subjects were encouraged to focus on the mental motor imagery and maintain the blue bars above the red bars, i.e. creating a higher ERD. For either right hand or left hand motor imagery, subjects were instructed to focus attention to the ERD amplitudes of the contralateral hemisphere: the left two columns for right hand motor imagery calculated from the electrodes over the left hemisphere, and the right two columns for left hand motor imagery calculated from the electrodes over the right hemisphere. The blue feedback bars for the ERD amplitudes were removed from the screen as the next high pitch tone was delivered. This cortical feedback was provided for subjects in the intervention group only. The subjects in the control group were also asked to look at the computer monitor though no feedback was provided. 2.4. Outcome measurement and statistical analysis Motor excitability obtained by EEG rhythmic power magnitude was used as the outcome measurement. The motor excitability is the corticomotoneuronal excitability to the stimulated body part (Kaelin-Lang et al., 2002), which is used for assessing stroke recovery (Laaksonen et al., 2012; Liepert, 2006). A theoretical study by (Steriade & Llinas, 1988) suggested that increased cellular excitability in the thalamo-cortical systems results in a low frequency power magnitude of cortical rhythms, which was considered as the result of involvement of a larger neural network or more cell assemblies in information processing or movement (Pfurtscheller & Lopes da Silva, 1999). Based upon this, the EEG rhythmic frequency power
magnitude in the beta band (16–28 Hz) during the production of voluntary movement was used for estimating motor excitability. The EEG rhythmic frequency power magnitude for representing motor excitability were calculated from the electrode C3 on the left motor cortex, which was averaged from 50 repeated physical executions of wrist extension on the right hand. The motor excitability measured from EEG rhythmic amplitudes was collected during the first visit before the motor imagery-based mental practice training began and at the fifth visit after the mental practice training was completed. A significance level of 0.05 was adopted in the analysis. The statistical analysis was performed using SPSS (Ver. 18, Chicago, IL).
3. Results The EEG frequency power magnitudes in the beta band associated with physical wrist extension for indexing the motor excitability are shown in Fig. 2. The left two columns show the frequency power magnitudes before and after motor imagery-based mental practice with cortical feedback (intervention group). The two columns on the right show the frequency power magnitudes before and after motor imagery-based mental practice without cortical feedback (control group). Before motor imagery-based mental practice there was no significant mean difference of the EEG frequency power magnitudes between the intervention group and the control group (t = 0.623, df = 8, p = 0.55, two tails). A significant change of the EEG rhythmic frequency power magnitude before and after the motor imagery-based mental practice was only observed in the intervention group (t = 2.237, df = 8, p = 0.028, one tail): the power magnitude in the beta band was significantly smaller following mental practice training than before the training. The change of the EEG rhythmic frequency power magnitude before and after the motor imagery-based mental practice in the control group was not significant (t = 0.272, df = 8, p = 0.604, one tail). A paired-t test was also performed to compare the difference between the intervention group and the control groups before and after motor imagery-based mental practice training. The decrease in EEG rhythmic frequency power magnitude was significantly larger in the intervention group who received cortical feedback training than that of the control group who received motor imagery training alone without feedback (t = 2.034, df = 8, p = 0.0382, one tail).
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4. Conclusion and discussion The results of this study demonstrate the efficacy of cortical feedback using ERD in motor imagery-based mental practice training. The motor excitability during the production of human voluntary movement can be effectively facilitated after motor imagery-based mental practice training with cortical feedback. Considering that many individuals who have experienced a CVA may be too weak to produce voluntary movement during the acute post-stroke phase of recovery, an efficient motor imagery-based mental practice training strategy can provide the opportunity to begin early motor rehabilitation efforts. ERD feedback training has been primarily employed in promoting the efficiency of a BCI-based device control. Wolpaw and McFarland trained individuals with tetraplegia to control cursor motion on a computer monitor. They found that after extensive training, patients learned to modulate their EEG rhythms in order to effect accurate movement of the cursor on the monitor screen. They were not able to exert reliable control at beginning of the training (Wolpaw & McFarland, 2004). Because the cursor movement is controlled by the amplitude of EEG rhythms in the alpha and beta bands over sensorimotor areas associated with human motor control, patients might enhance their ability to block EEG rhythmic power after extensive training. This finding indicates that ERD relevant to EEG power blocking or decrease may be enhanced after extensive training. In this study, we performed a relatively wellcontrolled trial to investigate the efficacy of the motor imagery-based mental practice training with cortical feedback by comparing the training effect between an intervention group that received motor imagery mental practice training with cortical feedback and a control group that received the training without feedback. The significant decrease in EEG frequency power magnitude seen in the intervention group after training suggests that real-time cortical feedback during motor imagery-based mental practice training can effectively promote the cortical excitability responsible for human voluntary movement. The efficacy demonstrated in this study highlights a potential new application of BCI technology in the rehabilitation process for individuals who have experienced brain injury. A major limitation of this study is the method for outcome measurement of motor excitability in human voluntary movement. In this study, the EEG rhythmic frequency power magnitude was used to estimate motor
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excitability by considering that a decreased and/or more widespread EEG rhythmic amplitude may indicate the involvement of a larger neural network or more cell assemblies in information processing or motor behavior production. Such consideration is suggested by a transcranial magnetic stimulation (TMS) study of human self-paced and simple reaction tasks (Chen, Yaseen, Cohen, & Hallett, 1998). The authors reported that the cortical excitability measured by motor evoked potential (MEP) was increased before and during the production of motor tasks. Because the rhythmic amplitude decrease occurs during the same time period relative to movement, the decrease of EEG rhythmic power magnitude may suggest an increase in cortical excitability. A recent study also demonstrated an increase in cortical excitability after BCI-based motor imagery training through a TMS study, though no comparison to baseline control was reported (Pichiorri et al., 2011). The results of this investigation are consistent with the findings of Pichiorri’s study. This is a proof-of-concept study aiming to demonstrate the feasibility of a novel motor imagery-based mental practice training strategy using real-time feedback of cortical activity via EEG. Though we used motor excitability as the outcome measurement, we still do not know the efficacy of the proposed mental practice training strategy with regard to the complex functional motor behaviors (e.g. mobility and activities of daily living) targeted in a rehabilitation program. The next step is to explore behavioral outcomes following motor imagery-based mental practice training with cortical activity feedback. Additional limitations of this study include the small sample size that includes only healthy subjects of a small, specific age range. These will be addressed in the future study. Declaration of interest The authors have no competing interest. References Annett, J. (1995). Motor imagery: Perception or action? Neuropsychologia, 33, 1395-1417. Azab, M., Al-Jarrah, M., Nazzal, M., Maayah, M., Sammour, M. A., & Jamous, M. (2009). Effectiveness of constraint-induced movement therapy (CIMT) as home-based therapy on Barthel Index in patients with chronic stroke. Top Stroke Rehabil, 16, 207-211. Bai, O., Lin, P., Vorbach, S., Floeter, M. K., Hattori, N., & Hallett, M. (2008). A high performance sensorimotor beta rhythm-based
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brain-computer interface associated with human natural motor behavior. J Neural Eng, 5, 24-35. Bai, O., Lin, P., Vorbach, S., Li, J., Furlani, S., & Hallett, M. (2007). Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clin Neurophysiol, 118, 2637-2655. Bailey, M. J., Riddoch, M. J., & Crome, P. (2002). Treatment of visual neglect in elderly patients with stroke: A single-subject series using either a scanning and cueing strategy or a left-limb activation strategy. Phys Ther, 82, 782-797. Berman, B. D., Horovitz, S. G., Venkataraman, G., & Hallett, M. (2012). Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback. Neuroimage, 59, 917-925. Bonifer, N. M., Anderson, K. M., & Arciniegas, D. B. (2005). Constraint-induced movement therapy after stroke: Efficacy for patients with minimal upper-extremity motor ability. Arch Phys Med Rehabil, 86, 1867-1873. Brogardh, C., & Sjolund, B. H. (2006). Constraint-induced movement therapy in patients with stroke: A pilot study on effects of small group training and of extended mitt use. Clin Rehabil, 20, 218227. Butler, A. J., & Page, S. J. (2006). Mental practice with motor imagery: Evidence for motor recovery and cortical reorganization after stroke. Arch Phys Med Rehabil, 87, S2-11. Chen, R., Yaseen, Z., Cohen, L. G., & Hallett, M. (1998). Time course of corticospinal excitability in reaction time and self-paced movements. Ann Neurol, 44, 317-325. Daley, K., Mayo, N., & Wood-Dauphinee, S. (1999). Reliability of scores on the Stroke Rehabilitation Assessment of Movement (STREAM) measure. Phys Ther, 79, 8-19; quiz 20-13. Dickstein, R., & Deutsch, J. E. (2007). Motor imagery in physical therapist practice. Phys Ther, 87, 942-953. Dohle, C., Pullen, J., Nakaten, A., Kust, J., Rietz, C., & Karbe, H. (2009). Mirror therapy promotes recovery from severe hemiparesis: A randomized controlled trial. Neurorehabil Neural Repair, 23, 209-217. Facchini, S., Muellbacher, W., Battaglia, F., Boroojerdi, B., & Hallett, M. (2002). Focal enhancement of motor cortex excitability during motor imagery: A transcranial magnetic stimulation study. Acta Neurol Scand, 105, 146-151. Fugl-Meyer, A. R., Jaasko, L., Leyman, I., Olsson, S., & Steglind, S. (1975). The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med, 7, 1331. Guan, C. (2009). A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain-Computer Interfaces. IEEE Trans Biomed Eng, 56, 2730-2733. Guillot, A., & Collet, C. (2005). Contribution from neurophysiological and psychological methods to the study of motor imagery. Brain Res Brain Res Rev, 50, 387-397. Hallett, M. (2000). Transcranial magnetic stimulation and the human brain. Nature, 406, 147-150. Hanakawa, T., Dimyan, M. A., & Hallett, M. (2008). Motor planning, imagery, and execution in the distributed motor network: A time-course study with functional MRI. Cereb Cortex, 18, 2775-2788. Huang, D., Lin, P., Fei, D. Y., Chen, X., & Bai, O. (2009). Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control. J Neural Eng, 6, 046005.
Jasper, H. H., & Andrews, H. L. (1938). Electro-encephalography. III. Normal differentiation of occipital and precentral regions in man. Arch Neurol Psychiat, 39, 95-115. Jeannerod, M., & Decety, J. (1995). Mental motor imagery: A window into the representational stages of action. Curr Opin Neurobiol, 5, 727-732. Kaelin-Lang, A., Luft, A. R., Sawaki, L., Burstein, A. H., Sohn, Y. H., & Cohen, L. G. (2002). Modulation of human corticomotor excitability by somatosensory input. J Physiol, 540, 623-633. Kimberley, T. J., Khandekar, G., Skraba, L. L., Spencer, J. A., Van Gorp, E. A., & Walker, S. R. (2006). Neural substrates for motor imagery in severe hemiparesis. Neurorehabil Neural Repair, 20, 268-277. Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nat Rev Neurosci, 2, 635-642. Laaksonen, K., Kirveskari, E., Makela, J. P., Kaste, M., Mustanoja, S., & Nummenmaa, L. et al. (2012). Effect of afferent input on motor cortex excitability during stroke recovery. Clin Neurophysiol, 123, 2429-2436. Langhorne, P. (2004). Review: Comprehensive occupational therapy interventions improve outcomes after stroke. ACP J Club, 140, 7. Liepert, J. (2006). Motor cortex excitability in stroke before and after constraint-induced movement therapy. Cogn Behav Neurol, 19, 41-47. Liu, K. P., Chan, C. C., Lee, T. M., & Hui-Chan, C. W. (2004). Mental imagery for promoting relearning for people after stroke: A randomized controlled trial. Arch Phys Med Rehabil, 85, 1403-1408. Lotze, M., Braun, C., Birbaumer, N., Anders, S., & Cohen, L. G. (2003). Motor learning elicited by voluntary drive. Brain, 126, 866-872. Lotze, M., & Cohen, L. G. (2006). Volition and imagery in neurorehabilitation. Cogn Behav Neurol, 19, 135-140. Lotze, M., & Halsband, U. (2006). Motor imagery. J Physiol Paris, 99, 386-395. Lyle, R. C. (1981). A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res, 4, 483-492. McFarland, D. J., Miner, L. A., Vaughan, T. M., & Wolpaw, J. R. (2000). Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr, 12, 177-186. Michelon, P., Vettel, J. M., & Zacks, J. M. (2006). Lateral somatotopic organization during imagined and prepared movements. J Neurophysiol, 95, 811-822. Niemeier, J. P. (1998). The Lighthouse Strategy: Use of a visual imagery technique to treat visual inattention in stroke patients. Brain Inj, 12, 399-406. Niemeier, J. P., Cifu, D. X., & Kishore, R. (2001). The lighthouse strategy: Improving the functional status of patients with unilateral neglect after stroke and brain injury using a visual imagery intervention. Top Stroke Rehabil, 8, 10-18. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97-113. Page, S. J., Levine, P., & Leonard, A. C. (2005). Effects of mental practice on affected limb use and function in chronic stroke. Arch Phys Med Rehabil, 86, 399-402. Page, S. J., Levine, P., Sisto, S. A., & Johnston, M. V. (2001). Mental practice combined with physical practice for upper-limb motor deficit in subacute stroke. Phys Ther, 81, 1455-1462. Pfurtscheller, G. (1981). Central beta rhythm during sensorimotor activities in man. Electroencephalogr Clin Neurophysiol, 51, 253264.
O. Bai et al. / Effect of real-time cortical feedback in motor imagery training Pfurtscheller, G., & Aranibar, A. (1979). Evaluation of event-related desynchronization (ERD) preceding and following voluntary selfpaced movement. Electroencephalogr Clin Neurophysiol, 46, 138-146. Pfurtscheller, G., Leeb, R., Keinrath, C., Friedman, D., Neuper, C., & Guger, C. et al. (2006). Walking from thought. Brain Res, 1071, 145-152. Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin Neurophysiol, 110, 1842-1857. Pfurtscheller, G., Muller, G. R., Pfurtscheller, J., Gerner, H. J., & Rupp, R. (2003). ‘Thought’–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett, 351, 33-36. Pfurtscheller, G., Neuper, C., Brunner, C., & da Silva, F. L. (2005). Beta rebound after different types of motor imagery in man. Neurosci Lett, 378, 156-159. Pichiorri, F., De Vico Fallani, F., Cincotti, F., Babiloni, F., Molinari, M., & Kleih, S. C. et al. (2011). Sensorimotor rhythm-based brain-computer interface training: The impact on motor cortical responsiveness. J Neural Eng, 8, 025020. Poole, J. L. (1991). Application of motor learning principles in occupational therapy. Am J Occup Ther, 45, 531-537. Rossini, P. M., Rossi, S., Pasqualetti, P., & Tecchio, F. (1999). Corticospinal excitability modulation to hand muscles during movement imagery. Cereb Cortex, 9, 161-167. Sharma, N., Pomeroy, V. M., & Baron, J. C. (2006). Motor imagery: A backdoor to the motor system after stroke? Stroke, 37, 1941-1952.
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Sharma, N., Simmons, L. H., Jones, P. S., Day, D. J., Carpenter, T. A., & Pomeroy, V. M. et al. (2009). Motor imagery after subcortical stroke: A functional magnetic resonance imaging study. Stroke, 40, 1315-1324. Sirigu, A., Cohen, L., Duhamel, J. R., Pillon, B., Dubois, B., & Agid, Y. et al. (1995). Congruent unilateral impairments for real and imagined hand movements. Neuroreport, 6, 997-1001. Smania, N., Bazoli, F., Piva, D., & Guidetti, G. (1997). Visuomotor imagery and rehabilitation of neglect. Arch Phys Med Rehabil, 78, 430-436. Steriade, M., & Llinas, R. R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiol Rev, 68, 649-742. Volpe, B. T., Huerta, P. T., Zipse, J. L., Rykman, A., Edwards, D., & Dipietro, L. et al. (2009). Robotic devices as therapeutic and diagnostic tools for stroke recovery. Arch Neurol, 66, 1086-1090. Welch, P. D. (1967). The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Trans Audio Electroacoust, AU-15, 70-73. Wolf, S. L., Winstein, C. J., Miller, J. P., Thompson, P. A., Taub, E., & Uswatte, G. et al. (2008). Retention of upper limb function in stroke survivors who have received constraint-induced movement therapy: The EXCITE randomised trial. Lancet Neurol, 7, 33-40. Wolpaw, J. R., & McFarland, D. J. (2004). Control of a twodimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A, 101, 1784917854.
NeuroRehabilitation 34 (2014) 355–363 DOI:10.3233/NRE-131039 IOS Press
Effect of real-time cortical feedback in motor imagery-based mental practice training Ou Baia,∗ , Dandan Huanga , Ding-Yu Feia and Richard Kunzb a EEG
& BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA b Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, USA
Abstract. BACKGROUND: Mental practice using motor imagery of limb movement may facilitate motor recovery in persons who have experienced cerebrovascular accident (CVA). However, the lack of a feedback mechanism that can monitor the quality of the motor imagery affects patients’ engagement and motivation to participate in the mental practice training program. OBJECTIVE: This study investigates the effect of novel real-time motor imagery-associated cortical activity feedback on motor imagery-based mental practice training. METHODS: Ten healthy volunteers were randomly assigned into intervention and control groups. Both groups participated in a five-visit motor imagery-based mental practice training program managed over a period of two months. The intervention group received mental practice training with real-time feedback of movement-associated cortical activity—beta band (16–28 Hz) event-related desynchronization (ERD) in electroencephalography (EEG), using a novel custom-made brain-computer interface (BCI) system. The control group received the mental practice training program without EEG cortical feedback. Motor excitability was assessed by measuring the frequency power magnitude of the EEG rhythmic activity associated with physical execution of wrist extension before and after the motor imagery-based mental practice training. RESULTS: The EEG frequency power magnitude associated with the physical execution of wrist extension was significantly lower (i.e. more desynchronized) after the mental practice training in the intervention group that received real-time cortical feedback (P < 0.05), whereas no significant difference in EEG frequency power magnitude associated with the physical execution of wrist extension was observed before and after mental practice training in the control group who did not receive feedback. CONCLUSIONS: The mental practice training program with motor imagery-associated cortical feedback facilitated motor excitability during the production of voluntary motor control. Motor imagery-based mental practice training with movementassociated cortical activity feedback may provide an effective strategy to facilitate motor recovery in brain injury patients, particularly during the early rehabilitation stage when full participation in physical and occupational therapy programs may not be possible due to excessive motor weakness. Keywords: Motor imagery, mental practice, motor control, electroencephalography (EEG), brain-computer interface (BCI), eventrelated desynchronization (ERD)
1. Introduction
∗ Address
for correspondence: Ou Bai, Ph.D., Director of EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, 401 West Main Street, Room 1252, Richmond, VA 23284-3067, P.O. Box 843067, USA. Tel.: +1 804 827 3607; Fax: +1 804 828 4454; E-mail: [email protected].
With the aging population and improved survival after initial injury, the prevalence and incidence of individuals living with disability following cerebral vascular accident (CVA) has increased (Volpe et al., 2009). These individuals will learn or relearn competencies necessary to perform activities of daily living
1053-8135/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved
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(ADL). Traditionally, the practice of skills provided in neurologic rehabilitation has focused on reducing motor impairment and minimizing physical disability (Langhorne, 2004; Poole, 1991). New rehabilitative approaches emphasize repetitive, task-specific practice with the affected arm, such as the contralateral limb restraint practice (Azab et al., 2009; Brogardh & Sjolund, 2006). Other novel methods to facilitate motor recovery use either an externally generated magnetic field targeting the brain (e.g. transcranial magnetic stimulation) or an externally generated electrical stimulus (e.g. functional or neuromuscular electrical stimulation) targeting the muscles of the upper and lower limbs (Hallett, 2000). Mirror therapy, in which the unaffected limb is presented to the individual as the affected limb using a reflection in the midsagittal plane, has also been employed as a rehabilitation tool (Dohle et al., 2009). These emerging methods aim to restore motor control through a change in brain function, i.e. motor relearning (Bonifer, Anderson, & Arciniegas, 2005; Wolf et al., 2008). Although promising, there are a large proportion of persons who have experienced a CVA for whom none of these methods has restored functional motor control. It is important to explore new methods that may facilitate recovery of brain function and the restoration of more normal motor control. Recently, an emerging method has been explored that employs mental practice using motor imagery of limb movement to facilitate motor recovery in persons who have experienced a CVA. This technique consists of training patients to rehearse a dynamic mental image of the desired motor output, typically in a hemiparetic limb. The mental motor imagery of limb movement is a complex cognitive operation involving self-generated intent using sensory and perceptual processes with outcomes of specific motor actions within working memory (Annett, 1995; Jeannerod & Decety, 1995; Kosslyn, Ganis, & Thompson, 2001; Michelon, Vettel, & Zacks, 2006). Scientific rationale within motor imagery-based mental training is established on: (1) the motor imagery incorporates a voluntary drive (Kimberley et al., 2006), an essential factor for motor learning after neurological injury (Lotze, Braun, Birbaumer, Anders, & Cohen, 2003); and (2) physically executed and imagined movements share a common neural substrate. It follows that the motor imagery-based mental practice training may also facilitate motor recovery similar to the physical training (Facchini, Muellbacher, Battaglia, Boroojerdi, & Hallett, 2002; Rossini, Rossi, Pasqualetti, & Tecchio, 1999; Sharma, Pomeroy, & Baron, 2006). Motor
imagery mental practice may contribute to the activation of neural descending and ascending loops of movement pathways which are represented in the brain with a kind of motor print (Butler & Page, 2006). There is also evidence of neural reorganization as a result of motor imagery mental practice training (Lotze & Cohen, 2006; Lotze & Halsband, 2006). Motor imagery-based mental practice might be incorporated into multidisciplinary rehabilitation programs as an adjunct to physical and occupational therapy programs for individuals recovering from CVA. Such adjunctive programs will probably be most effective during the early stage of recovery when neural plasticity and reorganization is most prominent (Sharma et al., 2006) and when motor execution training is still not fully possible due to patients’ excessive muscle weakness (Lotze & Cohen, 2006). There is evidence demonstrating the efficacy of such motor imagery-based mental practice training strategies. A case study reported on an individual patient with upper-limb hemiparesis who received a program combining physical therapy for the affected arm with mental practice during the sub-acute phase of recovery. The mental practice training consisted of listening to an audiotape instructing them to imagine functionally using their affected limb (Page, Levine, Sisto, & Johnston, 2001). After 6 weeks training, the patient exhibited reduction in impairment measured by the Fugl-Meyer scale (Fugl-Meyer, Jaasko, Leyman, Olsson, & Steglind, 1975) and improvement in arm function measured by the Action Research Arm Test (ARAT) (Lyle, 1981), and the Stroke Rehabilitation Assessment of Movement (STREAM) (Daley, Mayo, & Wood-Dauphinee, 1999). The investigators suggest that “mental practice may complement physical therapy to improve motor function after stroke.” Smania and colleagues demonstrated that visuomotor imagery training was effective “in diminishing impairment and functional disability associated with contralateral neglect” (Smania, Bazoli, Piva, & Guidetti, 1997). Randomized, controlled trials have shown that individuals in the chronic phase (greater than six weeks after injury) of recovery following stroke who mentally rehearsed functional activities of daily living showed significant improvement of Functional Independence Measure (FIM) and ARAT scores compared with individuals who received physical training only (Liu, Chan, Lee, & Hui-Chan, 2004; Page, Levine, & Leonard, 2005). Additionally, Niemeier’s group (Niemeier, 1998; Niemeier, Cifu, & Kishore, 2001) as well as Bailey’s group (Bailey, Riddoch, & Crome,
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2002) have demonstrated that mental training using a “Lighthouse Strategy” of turning the head to scan left and right in order to address visual neglect significantly improved functional capabilities in route finding and problem solving among persons who have experienced CVA. The critical barrier to achieving efficient motor imagery-based mental practice training for motor recovery is the lack of a feedback mechanism that can monitor the quality/performance of the motor imagery. This may affect patients’ engagement and motivation to participate in the mental practice training program. Both the clinician and individual recovering from CVA will benefit from knowing whether – and how effectively – they are engaged in the motor imagery-based mental practice. There are several objective measures available to provide such validation including dynamic brain imaging studies, autonomic monitoring, and mental chronometry. Dynamic brain imaging studies using fMRI have confirmed the neural substrates associated with the motor imagery mental practice as well as the neural reorganization that results from such mental practice (Berman, Horovitz, Venkataraman, & Hallett, 2012; Hanakawa, Dimyan, & Hallett, 2008; Kimberley et al., 2006; Sharma et al., 2009). However, fMRI is not practical as a real-time tool for monitoring the quality of motor imagery mental practice due to its long time-constant as well as the costly magnetic resonance equipment. Autonomic nervous system (ANS) activity recordings have been proposed for assessing motor imagery-based mental practice effort based upon that increased heart and respiratory rate may indicate an increase in mental effort (Guillot & Collet, 2005). However, such an analysis provides only an indirect assessment of central nervous system activity. In addition, because the aim of motor imagery-based mental practice is to activate motor networks, it is crucial that patients perform motor imagery of the self-generated movement tasks as opposed to non-movement related mental imagery (Sharma et al., 2006). However, movement and non-movement associated mental imagery tasks modulate similar ANS activities and may be indistinguishable with ANS monitoring. Mental chronometry for mental training is based on the observation that the duration of mentally simulated and physically executed motor tasks are comparable as the subjects produce mental images of motor tasks following an external metronome. There are some critical limitations to mental chronometry (Dickstein & Deutsch, 2007). First, it does not provide information about the vividness of motor imagery but
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rather the characteristic of time perseveration (Sirigu et al., 1995). Second, subjects may tend to count time instead of focusing on the motor imagery tasks (Sharma et al., 2006). Hence, the currently available technologies for monitoring/assessing motor imagery-based mental practice training remain insufficient. Human voluntary movement is associated with changes in cortical oscillations, which can be seen in scalp electroencephalography (EEG) and magnetoencephalography (MEG). Pfurtscheller and colleagues reported a sustained block or decrease of rhythmic oscillations, termed as event-related desynchronization (ERD) in alpha band (8–12 Hz) (Pfurtscheller & Aranibar, 1979) and in central beta-band (16–28 Hz) (Pfurtscheller, 1981) before and during the production of self-paced voluntary movements. ERD occurs not only associated with physical execution of voluntary movement, but also during the production of motor imagery of kinematic movements, i.e. subjects merely imagining their own body movement without actual physical execution (Huang, Lin, Fei, Chen, & Bai, 2009; McFarland, Miner, Vaughan, & Wolpaw, 2000; Pfurtscheller, Neuper, Brunner, & da Silva, 2005). Recently, a series of studies have reported the design of novel brain-computer interface (BCI) systems which allow subjects to drive a computer cursor to navigate a virtual world by performing motor imagery alone without actual physical movement (Guan, 2009; Huang et al., 2009; Pfurtscheller et al., 2006). Pfurtscheller and colleagues have also successfully applied the motor imagery strategy to control a functional electrical stimulation (FES) device to restore hand grasp in a patient with tetraplegia (Pfurtscheller, Muller, Pfurtscheller, Gerner, & Rupp, 2003). On one hand, these applications of motor imagery training create the possibility of driving assistive devices for the augmentation of motor function. On the other hand, these successful virtual controls via motor imagery also indicate that an EEG signal may provide a suitable indication to monitor the accuracy and quality of the motor imagery. In the present study, we have developed a novel brain-computer interface (BCI) system for online EEG activity feedback that provides a real-time indication of the motor imagery quality for mental practice training. The objective of this study is to determine the effect of real-time motor imagery-associated cortical feedback on the motor imagery-based mental practice training program. We hypothesized that the corticomotoneuronal motor excitability during the production of voluntary movement could be more efficiently promoted by the motor imagery-based mental practice
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training with cortical activity feedback than that without cortical feedback.
2. Methods 2.1. Subjects and group assignment Ten healthy volunteers (eight males and two females; age 20.8 ± 0.7 years old) participated in the study. Nine were right handed and one was left handed according to the Edinburgh inventory (Oldfield, 1971). None of the subjects had prior experience of similar feedback training. The protocol was approved by the Institutional Review Board, and all subjects gave their written informed consent for the study. Subjects were randomly assigned into the intervention and control groups. The assignment was even for the two groups. The subjects in the intervention group received motor imagery-based mental practice training with cortical activity feedback via EEG; the subjects in the control group received motor imagery-based mental practice training only, without cortical activity feedback.
including experiment setup (15–20 min) and six practice sessions was approximately 1.5 hours per visit. Subjects in both the intervention and control groups were asked to perform repeated motor imagery of kinematic hand movement following an audio cue provided by the custom-made cortical activity feedback system. At the first visit before the mental practice training program began, subjects in both groups were instructed to create a mental image in 1st-person perspective of alternating wrist extension from the neutral position, imagining also the sensations associated with muscle extension in their forearm. During the mental training, subjects initiated motor imagery of this wrist extension following a high pitch tone (9000 Hz, duration = 0.05 s), imagining that they were extending their wrist fully and sustaining the extended position for 2 seconds until they were presented with a low pitch tone (3000 Hz, duration = 0.05 s). Then subjects were instructed to maintain a mental image of the wrist relaxed in the neutral position for 2.5 seconds until the next high pitch tone was presented to repeat the cycle. Subjects repeated the same motor task 50 times during a single session.
2.2. Experimental and training protocol
2.3. Brain-computer interface system for cortical feedback
Subjects were seated in a chair with the tested forearm supported by a pillow. They were asked to alternately initiate and cease a motor imagery task of wrist movement (detailed description in the following paragraphs) according to auditory cues provided by a custom-made feedback system. Subjects were instructed to keep all muscles relaxed throughout the mental practice training sessions. In particular, when performing motor imagery, subjects were asked not to move their hand. One of the investigators monitored them visually to confirm no physical hand movement. Subjects were instructed to avoid ocular movements, eye blinking, postural adjustments, throat clearing, and other body or limb movements. The training program consisted of five separate visits, which were managed over a period of 2 months. There were 1–2 weeks between two consecutive visits according to subjects’ availability for the study. At each visit, the experimental procedure consisted of six motor imagery-based mental practice sessions; three sessions of right hand kinematic motor imagery and three sessions of left hand kinematic motor imagery. Each session lasted about 5–6 min with a 2–3 min break between two consecutive sessions. The total duration
A novel custom-made brain-computer interface (BCI) system was developed for EEG acquisition and processing to provide “online” cortical activity feedback in real-time. The diagram of the BCI-based feedback system is illustrated in Fig. 1. EEG was read from 4 (tin) surface electrodes (C3, C1, C2, C4) situated over motor cortex according to the international 10-20 system (Jasper & Andrews, 1938), which were mounted on an elastic cap (Electro-Cap International, Inc., Eaton, OH, USA). The four-channel EEG signals were referenced to a single electrode of Fz. The distance between two adjacent electrodes was approximately 2.5 cm. EEG signals from all channels were amplified, filtered (DC-100 Hz), and digitized (sampling frequency, 256 Hz) in a commercial EEG amplifier (g.tec GmgH, Schiedlberg, Austria). The EEG digital signals were then sent to a Hewlett Packard PC workstation equipped with a 2.33 GHz Xeon CPU and were processed in real time using a home-made MATLAB (MathWorks, Natick, MA) Toolbox: brain-computer interface to virtual reality or BCI2VR (Bai et al., 2007, 2008). The frequency power for ERD estimation was calculated online in realtime by self-made MATLAB scripts embedded in the
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Fig. 1. Schematic diagram of the BCI-based cortical feedback system for motor imagery-based mental practice training. Four EEG channels placed over motor cortex areas were amplified and digitized in a commercial EEG amplifier and then the computer converted the EEG signal into frequency power. The beta band ERD for the four EEG channels was extracted and fed back to the participants of the intervention group on a computer monitor in real-time. The participants were asked to create vivid kinematic mental motor imagery of their right and left wrist movement such that the blue bars on the monitor were lower than the red line, i.e. a higher ERD.
Fig. 2. Paired-t tests of EEG rhythmic frequency power magnitudes associated with the physical execution of right wrist extension. For the intervention group, the EEG rhythmic frequency power magnitude was significantly lower (P < 0.05) after the motor imagery-based mental practice training with cortical feedback compared to pre-training measurement. There was no significant before-after difference of the EEG rhythmic frequency power magnitude in the control group that received training without cortical feedback.
BCI2VR. The Welch method with a Hamming window was employed for the power spectral density (PSD) estimation in order to reduce the estimation variance and side-lobe effect (Welch, 1967); single-trial data in the selected time-window was segmented and periodograms from all segments were averaged to obtain a smoothed estimation. A 4 Hz frequency resolution, or
segment length of 256/4 = 64 under 50% overlapping, was used in Welch-based PSD estimation. Beta band power from 16–28 Hz was extracted for feedback. The beta power was converted to decibel scales. ERD was the difference between the decibel power in the ERD window and baseline power; a higher amplitude of ERD indicates a smaller amplitude of rhythmic activity in
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the ERD windows compared with the baseline activity. The estimated ERD amplitudes of the four EEG channels over motor areas were then fed back to the subject on a computer monitor that was situated approximately 1.5 m before the subject. The cortical ERD feedback system also saved the online ERD data for offline analysis. As shown in Fig. 1, the cortical ERD activity of the four EEG channels was fed back to the subjects on a computer monitor by four column panels. When a low pitch tone was delivered, the subjects ceased motor imagery and the ERD was provided on the monitor immediately. The blue bars represent real-time ERD amplitudes estimated from the EEG activity associated with the motor imagery performed. The red bars represent a preset target value for ERD, i.e. 50% of the ERD associated with physical wrist extension obtained in the first visit. Subjects were encouraged to focus on the mental motor imagery and maintain the blue bars above the red bars, i.e. creating a higher ERD. For either right hand or left hand motor imagery, subjects were instructed to focus attention to the ERD amplitudes of the contralateral hemisphere: the left two columns for right hand motor imagery calculated from the electrodes over the left hemisphere, and the right two columns for left hand motor imagery calculated from the electrodes over the right hemisphere. The blue feedback bars for the ERD amplitudes were removed from the screen as the next high pitch tone was delivered. This cortical feedback was provided for subjects in the intervention group only. The subjects in the control group were also asked to look at the computer monitor though no feedback was provided. 2.4. Outcome measurement and statistical analysis Motor excitability obtained by EEG rhythmic power magnitude was used as the outcome measurement. The motor excitability is the corticomotoneuronal excitability to the stimulated body part (Kaelin-Lang et al., 2002), which is used for assessing stroke recovery (Laaksonen et al., 2012; Liepert, 2006). A theoretical study by (Steriade & Llinas, 1988) suggested that increased cellular excitability in the thalamo-cortical systems results in a low frequency power magnitude of cortical rhythms, which was considered as the result of involvement of a larger neural network or more cell assemblies in information processing or movement (Pfurtscheller & Lopes da Silva, 1999). Based upon this, the EEG rhythmic frequency power
magnitude in the beta band (16–28 Hz) during the production of voluntary movement was used for estimating motor excitability. The EEG rhythmic frequency power magnitude for representing motor excitability were calculated from the electrode C3 on the left motor cortex, which was averaged from 50 repeated physical executions of wrist extension on the right hand. The motor excitability measured from EEG rhythmic amplitudes was collected during the first visit before the motor imagery-based mental practice training began and at the fifth visit after the mental practice training was completed. A significance level of 0.05 was adopted in the analysis. The statistical analysis was performed using SPSS (Ver. 18, Chicago, IL).
3. Results The EEG frequency power magnitudes in the beta band associated with physical wrist extension for indexing the motor excitability are shown in Fig. 2. The left two columns show the frequency power magnitudes before and after motor imagery-based mental practice with cortical feedback (intervention group). The two columns on the right show the frequency power magnitudes before and after motor imagery-based mental practice without cortical feedback (control group). Before motor imagery-based mental practice there was no significant mean difference of the EEG frequency power magnitudes between the intervention group and the control group (t = 0.623, df = 8, p = 0.55, two tails). A significant change of the EEG rhythmic frequency power magnitude before and after the motor imagery-based mental practice was only observed in the intervention group (t = 2.237, df = 8, p = 0.028, one tail): the power magnitude in the beta band was significantly smaller following mental practice training than before the training. The change of the EEG rhythmic frequency power magnitude before and after the motor imagery-based mental practice in the control group was not significant (t = 0.272, df = 8, p = 0.604, one tail). A paired-t test was also performed to compare the difference between the intervention group and the control groups before and after motor imagery-based mental practice training. The decrease in EEG rhythmic frequency power magnitude was significantly larger in the intervention group who received cortical feedback training than that of the control group who received motor imagery training alone without feedback (t = 2.034, df = 8, p = 0.0382, one tail).
O. Bai et al. / Effect of real-time cortical feedback in motor imagery training
4. Conclusion and discussion The results of this study demonstrate the efficacy of cortical feedback using ERD in motor imagery-based mental practice training. The motor excitability during the production of human voluntary movement can be effectively facilitated after motor imagery-based mental practice training with cortical feedback. Considering that many individuals who have experienced a CVA may be too weak to produce voluntary movement during the acute post-stroke phase of recovery, an efficient motor imagery-based mental practice training strategy can provide the opportunity to begin early motor rehabilitation efforts. ERD feedback training has been primarily employed in promoting the efficiency of a BCI-based device control. Wolpaw and McFarland trained individuals with tetraplegia to control cursor motion on a computer monitor. They found that after extensive training, patients learned to modulate their EEG rhythms in order to effect accurate movement of the cursor on the monitor screen. They were not able to exert reliable control at beginning of the training (Wolpaw & McFarland, 2004). Because the cursor movement is controlled by the amplitude of EEG rhythms in the alpha and beta bands over sensorimotor areas associated with human motor control, patients might enhance their ability to block EEG rhythmic power after extensive training. This finding indicates that ERD relevant to EEG power blocking or decrease may be enhanced after extensive training. In this study, we performed a relatively wellcontrolled trial to investigate the efficacy of the motor imagery-based mental practice training with cortical feedback by comparing the training effect between an intervention group that received motor imagery mental practice training with cortical feedback and a control group that received the training without feedback. The significant decrease in EEG frequency power magnitude seen in the intervention group after training suggests that real-time cortical feedback during motor imagery-based mental practice training can effectively promote the cortical excitability responsible for human voluntary movement. The efficacy demonstrated in this study highlights a potential new application of BCI technology in the rehabilitation process for individuals who have experienced brain injury. A major limitation of this study is the method for outcome measurement of motor excitability in human voluntary movement. In this study, the EEG rhythmic frequency power magnitude was used to estimate motor
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excitability by considering that a decreased and/or more widespread EEG rhythmic amplitude may indicate the involvement of a larger neural network or more cell assemblies in information processing or motor behavior production. Such consideration is suggested by a transcranial magnetic stimulation (TMS) study of human self-paced and simple reaction tasks (Chen, Yaseen, Cohen, & Hallett, 1998). The authors reported that the cortical excitability measured by motor evoked potential (MEP) was increased before and during the production of motor tasks. Because the rhythmic amplitude decrease occurs during the same time period relative to movement, the decrease of EEG rhythmic power magnitude may suggest an increase in cortical excitability. A recent study also demonstrated an increase in cortical excitability after BCI-based motor imagery training through a TMS study, though no comparison to baseline control was reported (Pichiorri et al., 2011). The results of this investigation are consistent with the findings of Pichiorri’s study. This is a proof-of-concept study aiming to demonstrate the feasibility of a novel motor imagery-based mental practice training strategy using real-time feedback of cortical activity via EEG. Though we used motor excitability as the outcome measurement, we still do not know the efficacy of the proposed mental practice training strategy with regard to the complex functional motor behaviors (e.g. mobility and activities of daily living) targeted in a rehabilitation program. The next step is to explore behavioral outcomes following motor imagery-based mental practice training with cortical activity feedback. Additional limitations of this study include the small sample size that includes only healthy subjects of a small, specific age range. These will be addressed in the future study. Declaration of interest The authors have no competing interest. References Annett, J. (1995). Motor imagery: Perception or action? Neuropsychologia, 33, 1395-1417. Azab, M., Al-Jarrah, M., Nazzal, M., Maayah, M., Sammour, M. A., & Jamous, M. (2009). Effectiveness of constraint-induced movement therapy (CIMT) as home-based therapy on Barthel Index in patients with chronic stroke. Top Stroke Rehabil, 16, 207-211. Bai, O., Lin, P., Vorbach, S., Floeter, M. K., Hattori, N., & Hallett, M. (2008). A high performance sensorimotor beta rhythm-based
362
O. Bai et al. / Effect of real-time cortical feedback in motor imagery training
brain-computer interface associated with human natural motor behavior. J Neural Eng, 5, 24-35. Bai, O., Lin, P., Vorbach, S., Li, J., Furlani, S., & Hallett, M. (2007). Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG. Clin Neurophysiol, 118, 2637-2655. Bailey, M. J., Riddoch, M. J., & Crome, P. (2002). Treatment of visual neglect in elderly patients with stroke: A single-subject series using either a scanning and cueing strategy or a left-limb activation strategy. Phys Ther, 82, 782-797. Berman, B. D., Horovitz, S. G., Venkataraman, G., & Hallett, M. (2012). Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback. Neuroimage, 59, 917-925. Bonifer, N. M., Anderson, K. M., & Arciniegas, D. B. (2005). Constraint-induced movement therapy after stroke: Efficacy for patients with minimal upper-extremity motor ability. Arch Phys Med Rehabil, 86, 1867-1873. Brogardh, C., & Sjolund, B. H. (2006). Constraint-induced movement therapy in patients with stroke: A pilot study on effects of small group training and of extended mitt use. Clin Rehabil, 20, 218227. Butler, A. J., & Page, S. J. (2006). Mental practice with motor imagery: Evidence for motor recovery and cortical reorganization after stroke. Arch Phys Med Rehabil, 87, S2-11. Chen, R., Yaseen, Z., Cohen, L. G., & Hallett, M. (1998). Time course of corticospinal excitability in reaction time and self-paced movements. Ann Neurol, 44, 317-325. Daley, K., Mayo, N., & Wood-Dauphinee, S. (1999). Reliability of scores on the Stroke Rehabilitation Assessment of Movement (STREAM) measure. Phys Ther, 79, 8-19; quiz 20-13. Dickstein, R., & Deutsch, J. E. (2007). Motor imagery in physical therapist practice. Phys Ther, 87, 942-953. Dohle, C., Pullen, J., Nakaten, A., Kust, J., Rietz, C., & Karbe, H. (2009). Mirror therapy promotes recovery from severe hemiparesis: A randomized controlled trial. Neurorehabil Neural Repair, 23, 209-217. Facchini, S., Muellbacher, W., Battaglia, F., Boroojerdi, B., & Hallett, M. (2002). Focal enhancement of motor cortex excitability during motor imagery: A transcranial magnetic stimulation study. Acta Neurol Scand, 105, 146-151. Fugl-Meyer, A. R., Jaasko, L., Leyman, I., Olsson, S., & Steglind, S. (1975). The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med, 7, 1331. Guan, C. (2009). A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain-Computer Interfaces. IEEE Trans Biomed Eng, 56, 2730-2733. Guillot, A., & Collet, C. (2005). Contribution from neurophysiological and psychological methods to the study of motor imagery. Brain Res Brain Res Rev, 50, 387-397. Hallett, M. (2000). Transcranial magnetic stimulation and the human brain. Nature, 406, 147-150. Hanakawa, T., Dimyan, M. A., & Hallett, M. (2008). Motor planning, imagery, and execution in the distributed motor network: A time-course study with functional MRI. Cereb Cortex, 18, 2775-2788. Huang, D., Lin, P., Fei, D. Y., Chen, X., & Bai, O. (2009). Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control. J Neural Eng, 6, 046005.
Jasper, H. H., & Andrews, H. L. (1938). Electro-encephalography. III. Normal differentiation of occipital and precentral regions in man. Arch Neurol Psychiat, 39, 95-115. Jeannerod, M., & Decety, J. (1995). Mental motor imagery: A window into the representational stages of action. Curr Opin Neurobiol, 5, 727-732. Kaelin-Lang, A., Luft, A. R., Sawaki, L., Burstein, A. H., Sohn, Y. H., & Cohen, L. G. (2002). Modulation of human corticomotor excitability by somatosensory input. J Physiol, 540, 623-633. Kimberley, T. J., Khandekar, G., Skraba, L. L., Spencer, J. A., Van Gorp, E. A., & Walker, S. R. (2006). Neural substrates for motor imagery in severe hemiparesis. Neurorehabil Neural Repair, 20, 268-277. Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nat Rev Neurosci, 2, 635-642. Laaksonen, K., Kirveskari, E., Makela, J. P., Kaste, M., Mustanoja, S., & Nummenmaa, L. et al. (2012). Effect of afferent input on motor cortex excitability during stroke recovery. Clin Neurophysiol, 123, 2429-2436. Langhorne, P. (2004). Review: Comprehensive occupational therapy interventions improve outcomes after stroke. ACP J Club, 140, 7. Liepert, J. (2006). Motor cortex excitability in stroke before and after constraint-induced movement therapy. Cogn Behav Neurol, 19, 41-47. Liu, K. P., Chan, C. C., Lee, T. M., & Hui-Chan, C. W. (2004). Mental imagery for promoting relearning for people after stroke: A randomized controlled trial. Arch Phys Med Rehabil, 85, 1403-1408. Lotze, M., Braun, C., Birbaumer, N., Anders, S., & Cohen, L. G. (2003). Motor learning elicited by voluntary drive. Brain, 126, 866-872. Lotze, M., & Cohen, L. G. (2006). Volition and imagery in neurorehabilitation. Cogn Behav Neurol, 19, 135-140. Lotze, M., & Halsband, U. (2006). Motor imagery. J Physiol Paris, 99, 386-395. Lyle, R. C. (1981). A performance test for assessment of upper limb function in physical rehabilitation treatment and research. Int J Rehabil Res, 4, 483-492. McFarland, D. J., Miner, L. A., Vaughan, T. M., & Wolpaw, J. R. (2000). Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr, 12, 177-186. Michelon, P., Vettel, J. M., & Zacks, J. M. (2006). Lateral somatotopic organization during imagined and prepared movements. J Neurophysiol, 95, 811-822. Niemeier, J. P. (1998). The Lighthouse Strategy: Use of a visual imagery technique to treat visual inattention in stroke patients. Brain Inj, 12, 399-406. Niemeier, J. P., Cifu, D. X., & Kishore, R. (2001). The lighthouse strategy: Improving the functional status of patients with unilateral neglect after stroke and brain injury using a visual imagery intervention. Top Stroke Rehabil, 8, 10-18. Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97-113. Page, S. J., Levine, P., & Leonard, A. C. (2005). Effects of mental practice on affected limb use and function in chronic stroke. Arch Phys Med Rehabil, 86, 399-402. Page, S. J., Levine, P., Sisto, S. A., & Johnston, M. V. (2001). Mental practice combined with physical practice for upper-limb motor deficit in subacute stroke. Phys Ther, 81, 1455-1462. Pfurtscheller, G. (1981). Central beta rhythm during sensorimotor activities in man. Electroencephalogr Clin Neurophysiol, 51, 253264.
O. Bai et al. / Effect of real-time cortical feedback in motor imagery training Pfurtscheller, G., & Aranibar, A. (1979). Evaluation of event-related desynchronization (ERD) preceding and following voluntary selfpaced movement. Electroencephalogr Clin Neurophysiol, 46, 138-146. Pfurtscheller, G., Leeb, R., Keinrath, C., Friedman, D., Neuper, C., & Guger, C. et al. (2006). Walking from thought. Brain Res, 1071, 145-152. Pfurtscheller, G., & Lopes da Silva, F. H. (1999). Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin Neurophysiol, 110, 1842-1857. Pfurtscheller, G., Muller, G. R., Pfurtscheller, J., Gerner, H. J., & Rupp, R. (2003). ‘Thought’–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett, 351, 33-36. Pfurtscheller, G., Neuper, C., Brunner, C., & da Silva, F. L. (2005). Beta rebound after different types of motor imagery in man. Neurosci Lett, 378, 156-159. Pichiorri, F., De Vico Fallani, F., Cincotti, F., Babiloni, F., Molinari, M., & Kleih, S. C. et al. (2011). Sensorimotor rhythm-based brain-computer interface training: The impact on motor cortical responsiveness. J Neural Eng, 8, 025020. Poole, J. L. (1991). Application of motor learning principles in occupational therapy. Am J Occup Ther, 45, 531-537. Rossini, P. M., Rossi, S., Pasqualetti, P., & Tecchio, F. (1999). Corticospinal excitability modulation to hand muscles during movement imagery. Cereb Cortex, 9, 161-167. Sharma, N., Pomeroy, V. M., & Baron, J. C. (2006). Motor imagery: A backdoor to the motor system after stroke? Stroke, 37, 1941-1952.
363
Sharma, N., Simmons, L. H., Jones, P. S., Day, D. J., Carpenter, T. A., & Pomeroy, V. M. et al. (2009). Motor imagery after subcortical stroke: A functional magnetic resonance imaging study. Stroke, 40, 1315-1324. Sirigu, A., Cohen, L., Duhamel, J. R., Pillon, B., Dubois, B., & Agid, Y. et al. (1995). Congruent unilateral impairments for real and imagined hand movements. Neuroreport, 6, 997-1001. Smania, N., Bazoli, F., Piva, D., & Guidetti, G. (1997). Visuomotor imagery and rehabilitation of neglect. Arch Phys Med Rehabil, 78, 430-436. Steriade, M., & Llinas, R. R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiol Rev, 68, 649-742. Volpe, B. T., Huerta, P. T., Zipse, J. L., Rykman, A., Edwards, D., & Dipietro, L. et al. (2009). Robotic devices as therapeutic and diagnostic tools for stroke recovery. Arch Neurol, 66, 1086-1090. Welch, P. D. (1967). The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms. IEEE Trans Audio Electroacoust, AU-15, 70-73. Wolf, S. L., Winstein, C. J., Miller, J. P., Thompson, P. A., Taub, E., & Uswatte, G. et al. (2008). Retention of upper limb function in stroke survivors who have received constraint-induced movement therapy: The EXCITE randomised trial. Lancet Neurol, 7, 33-40. Wolpaw, J. R., & McFarland, D. J. (2004). Control of a twodimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A, 101, 1784917854.
