This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2< features extraction and classification algorithms [20][22]. Features extraction based on fast Fourier transform (FFT) has been used widely in the EEG-based BCI. However, the Hilbert-Huang transform (HHT) has been shown to provide a better result compared to conventional FFT to tackle nonlinear and non-stationary signals of mental tasks for EEGbased BCI [19]. For the classification algorithm, artificial neural network (ANN) is known as a non-linear classification method and has been used to handle the mental task basedBCI [6], [18], [19]. A genetic algorithm-based ANN (GAANN) has been applied to optimize the ANN training in the mental task-based BCI [18], [19]. Recently, an improved optimization technique using fuzzy particle swarm optimization with the cross-mutated operation (FPSOCM) was proposed [23]. In FPSOCM, the fuzzy inertia weight that provides nonlinearity characteristics can offer an enhanced searching quality; the cross-mutated operation can effectively handle the drawback of trapping in local optima. This paper explores further use of the FPSCOCM-ANN algorithm in BCI applications. The contributions of this paper are as follows: firstly, this paper combines the neural-network classifier with the fuzzy particle swarm optimization with cross-mutated operation (FPSOCM-ANN) for a three-class of mental task-based BCI classification. The features extraction method is based on the Hilbert-Huang transform (HHT). For comparison purposes, different classifiers and feature extractors are included to find the best algorithms with the highest accuracy. The non-motor imagery mental tasks used are letter composing, arithmetic and figure Rubik’s cube rolling forward, which can be mapped for three wheelchair commands: left, right and forward. An additional eyes closed task is recorded as well for testing an on-off command. Secondly, most of the results of the mental task-based BCI were from experiments on able-bodied subjects only. This paper includes five able-bodied subjects and five patients with tetraplegia. Thirdly, different timewindows of data are investigated to find the best data windowing with an improved result of classification accuracy. Fourthly, for practical reasons, results of combinations of two channels classification are presented to find the two channels of EEG signals suitable for mental task-based BCI. The structure of this paper is as follows: section II covers the methodology: general structure, data collection, feature extraction methods and classification algorithms. Section III describes results and discussion, followed by section IV for the conclusion.
2 training and classification tasks. The outputs classification can be mapped to the three wheelchair steering commands.
Fig. 1. Components of mental task-based BCI
B. Experimental Data Collection The Human Research Ethics Committee of University of Technology, Sydney approved this study. A total of ten participants were involved: five able-bodied subjects (S1-S5) aged between 25 and 35 years; five patients with tetraplegia (T1-T5) aged between 45 and 80 years who suffer a high-level of SCI in the cervical area at level C3, C4, C5 and C6 with the details as shown in Table I and Fig. 2. TABLE I
ABLE BODIED SUBJECTS AND PATIENTS WITH TETRAPLEGIA DETAILS Participants S1, S2, S3,S4, S5 T1 T2 T3 T4 T5
Age 25-35 80 59 50 55 45
Description Able-bodied subjects Tetraplegia, cervical SCI at C3- C4 Tetraplegia, cervical SCI at C5- C6 Tetraplegia, cervical SCI at C3- C4 Tetraplegia, cervical SCI at C3- C4 Tetraplegia, cervical SCI at C5- C6
A commercial 32-channels EEG system, from Compumedics was used with the sampling rate of the system at 256 Hz. This study used 6 channels with the electrodes positioned at locations C3, C4, P3, P4, O1 and O2. The left earlobe (A1) was used as the reference and the GND electrode was attached to the right earlobe (A2) as shown in Fig 2. This configuration refers to the standard 10-20 electrode montage system [24]. During the experiment, EEG gel was applied; the electrode contact impedance was measured and maintained below 5kΩ. Participants were asked to keep eye blinks, and unnecessary movements to a minimum during the experiment. Data with strong presence of artifacts was discarded.
II. METHODOLOGY A. General Structure Fig. 1 illustrates the basic components for the non-motor imagery mental task-based BCI for this paper. This starts from data collection by using EEG, followed by a signal preprocessing module: window segmentation and digital filters. Next, a features extraction module transforms the signals into useful features. The features are processed into a classification algorithm, in this case an ANN which includes optimization,
Fig. 2. Experiment setup of EEG-BCI with patients with tetraplegia
As an offline study, a proper standard protocol is needed to ensure all participants perform tasks correctly. A total of three non-motor imagery mental tasks were used including mental letter composing, arithmetic and a figure of Rubik’s cube rolling. Prior to the beginning of the session, participants were shown a video as a guidance to perform mental tasks for the
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2
pcm then Perform cross-mutated operation output (ƒ(X(t)))
Time
Fig. 6. EMD process of eyes closed signal at 1s time-window
end return the best particle, g
120
end Fig. 4. The FPSOCM procedure.
(9)
where fitness denotes the fitness value and err is the mean square error (MSE). It can be seen from the defined formula that a larger fitness value implies a smaller MSE. For the performance measurement, classification accuracy was used in this paper as evaluation criteria. This is a widely used evaluation criteria in BCI. Comparisons of classifier are given including GA-ANN, support vector machine (SVM), linear discriminate analysis (LDA) and linear perceptron are given in the next section. III. RESULTS AND DISCUSSIONS A. Testing the HHT as the Features Extractor Initial testing was conducted for the HHT with a known signal of four combination sinusoid signals as defined in (6). The result is shown in Fig.5, EMD as the first step of HHT for this signal was composed correctly of five IMFs with the first four IMFs representing the four defined sinusoidal signals at different frequencies and the fifth IMF representing the residue. It has been known that during the eyes closed task, there is a dominant feature in the alpha band of EEG (8-13Hz) [30]. This unique feature can be used for further testing to ensure
Frequency (Hz)
100
The objective of the FPSOCM-ANN training is to minimize the fitness (cost) values interactively with the fitness function: fitness 1 / (1 err )
Time
80 60 40
20 Alpha 0
50
100 150 200 250 Count (1s =256 count) Fig. 7. HHT spectrum of eyes closed signal at a 1s time-window
The segmented EEG feature data (eyes closed task) was processed and converted into a series of IMFs and residue in the EMD process as shown in Fig.6. This was followed by applying the HT method to the IMFs resulting in the amplitude and instantaneous frequency as functions of time. The plotting of the HHT spectrum in Fig.7 for the eyes closed task shows a clear dominant feature of the instantaneous frequency of the alpha EEG band (8-13Hz). This proves the features extraction method has correctly converted EEG data for the eyes closed task into the proper feature. B. Classification using FPSOCM –ANN at different timewindows and Comparison with GA-ANN The training of the neural network was repeated 10 times for each different hidden neuron; thus, the reported accuracies were the mean value of 10 results of accuracies. The number of hidden neurons was varied from 4 to 30 units to obtain the
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2< bodied subjects (S1-S5) and five patients with tetraplegia (T1T5). Between 1s to 6s time-windows, the average accuracies for able-bodied subjects were increased in each higher timewindow between 67.3±5.7% and 76.5±7.9% using GA-ANN. These were improved using FPSOCM-ANN compared to GAANN with improved accuracies between 72.0±3.9% and 83.9±6.7%. Patients with tetraplegia resulted in accuracies of between 59.7±1.5% and 74.4±4.2% using GA-ANN and improved accuracies based on FPSOCM-ANN with an average accuracy of between 63.0±1.0% and 81.8±3.8%. The overall mean accuracy of three mental tasks classification at between 1s to 6s time-windows for both groups were between 63.5±5.6% and 75.5±6.1% with GA-ANN and improved accuracies between 67.5±5.5% and 82.9±5.2% were found with FPSOCM-ANN. The highest overall accuracy was reached at 7s timewindow with improved accuracies for both groups compared to previous time-windows. In details, the average accuracy of five able-bodied subjects was 79.0±8.6% using GA-ANN method and improved accuracy at 85.5±5.5% using FPSOCMANN. On five patients with tetraplegia, the average accuracy resulted at 75.9±5.3% using GA-ANN and this accuracy was increased using FPSOCM-ANN with an average accuracy at 83.3±6.0%. The overall average accuracy of three mental tasks classification for two groups combined at a 7s time-window was 77.4±6.9% using GA-ANN and improved accuracy at 84.4±5.5% using FPSOCM-ANN. Compared to the previous time-window, the overall accuracies at between 8s and 10s time windows were much more in a steady value. The average accuracies for ablebodied subjects were between 77.2±7.6% and 78.0±6.9% with GA-ANN. These were improved using FPSOCM-ANN compared to GA-ANN with accuracies of around 84.6±5.3%. For patients with tetraplegia, accuracies using GA-ANN were between 75.4±4.8 % and 76.9±6.4 and improved accuracies based on FPSOCM-ANN with an average accuracy of around 83.4±5.4%. The overall accuracies of three mental tasks classification at between 8s to 10s time-windows for both groups were between 76.5±6.7% and 77.1±6.6% with GAANN and improved accuracies of around 84.0±5.1% using FPSOCM-ANN. In general, for three mental task classifications, the results show an overall improved accuracy for the classification algorithm using FPSOCM-ANN compared to GA-ANN across both groups and in different time-windows. For comparison between able-bodied subjects and patients with tetraplegia, the patients’ group provided lower classification accuracy. However, the accuracy was improved by increasing the duration of the time-window with at best achieved accuracy for a 7s time-window of 77.4% based on GA-ANN and improved accuracy of 84.4% using FPSOCM-ANN. From this experiment, patients with tetraplegia tend to have better classification accuracy when increasing the time-window which means they need more time to perform the mental tasks due with their disability issue. This can be found using FPSOCM-ANN, where for a 1s time-window, patients with
6 tetraplegia achieve an accuracy of 63% compared with 72% for able-bodied subjects. The patients group had accuracy around 9% lower than the able-bodied group. At 7s timewindow, the accuracy difference between both groups was much smaller at only 2.2% with an accuracy for patients with tetraplegia of 83.3% and able-bodied subject of 85.5%. C. Further Comparison classifiers and features extractors Further comparisons are carried out between different classifiers and feature extractors as shown in Table III using the 7s time-window of data as the best time window. Due to the output classifier using three mental tasks, instead of using a binary classifier, the comparison classifiers need a multiclass model. These classifiers are: multi-class linear perceptron classifier with the Kesler’s construction [35]; multi-class linear discriminant analysis (LDA) with the fisher kernel [36], [37]; and multi-class support vector machine (SVM) using BSVM (B refers to the added bias) [35], [38]. For the feature extraction methods comparison, FFT [18] and wavelet [39] methods are presented. The discrete wavelet transform (DWT) with Daubechies type as the mother wavelet in a 5 level decomposition was used in accordance with previous BCI-EEG analyses [40], [41]. There are improvements on the original HHT especially with the EMD analysis available [42], [43]. The HHT using the ensemble empirical mode decomposition (EEMD) [43] was also included. The result shows the multi-class linear perceptron classifier provided an overall mean accuracy of: 63.7±2.5% using the FFT; 67.1±2.8% using the wavelet; 71±4.5% using the HHTEMD; and 71.7±3.8 % using the HHT-EEMD. The accuracies are slightly improved across different feature extractors when using the multi-class LDA classifier with the overall mean accuracy of: 64.8±3.3% using the FFT; 68.1±2.7% using the wavelet; 72.1±4.5% using the HHT-EMD; and 72.5±4.4% using the HHT-EEMD. The multi-class SVM classifier provided improved results across different features extractors to the previous two methods with overall mean accuracy of: 66.9±2.7% using FFT; 70.6±3.2% using wavelet; 77.0±6.6% using HHT-EMD; 63.7±2.5%; and 76.8±5.6% using HHTEEMD. For the GA-ANN classifier, the accuracy is further improved compare to previous classifiers with the overall accuracy of: 67.8±3.4% using FFT; 71.8±3.3% using wavelet; 77.4±6.9% using HHT-EMD; and 77.9±6.7% using the HHTEEMD. The FPSOCM-ANN classifier provided the most improvement with the overall mean accuracy of: 70.7±2.9% using FFT; 75.7±4.1% using wavelet; 84.4±5.5% using HHTEMD; 84.5±4.9% using HHT-EEMD. The neural networks classifiers (GA-ANN and FPSOCMANN) with the optimization methods in this study provide better results compared to the linear perceptron, LDA and SVM methods. The FPSOCM-ANN provided the best classifier among other classifiers. The HHT as the feature extractor has higher accuracy compared to FFT and wavelet methods. In the FPSOCM-ANN, it can also be seen that there is a comparable accuracy between HHT-EMD and HHTEEMD in this study, thus both methods can be used.
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2
TITB-00323-2013.R2
TITB-00323-2013.R2< signal. Further HHT testing for the eyes closed EEG signal shows a correct dominant alpha (8-13Hz) wave with high classification accuracy for both groups of participants. Results for three mental tasks classification show improved accuracy of FPSOCM-ANN compared to GA-ANN and other classifiers in multi-class mode (SVM, LDA and linear perceptron) across both groups and different time-windows. Although the patients group has lower classification accuracy, this is improved by increasing the time windows with the best classification accuracy achieved at a 7s time-window. For practical use of a BCI, combinations of two channels EEG have also been presented with the variation result of the best two channels in each subject. For overall generalization of both groups, O1and C4 are the best two channels, followed by the second best at P3 and O2, and the third best at C3 and O2 channels. For each correctly classified mental task, the arithmetic task has the highest rate of classification, followed by the rolling cube task and letter composing task. In the online application as future work, these three non-motor imagery mental tasks can be directly mapped into three wheelchair commands. For example the mental letter composing task is used for left command; arithmetic mental task for right command; and imagining a rolling cube for forward command. An additional eyes closed task can be used for on-off command. The users perform the imagery mental task at will which are initiated by users’ themselves (selfpaced) for the wheelchair control application.
10
[11]
[12]
[13] [14]
[15]
[16] [17]
[18]
[19]
REFERENCES [20] [1]
J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clin. Neurophysiol., vol. 113, pp. 767791, June 2002. [2] G. Schalk, "Brain–computer symbiosis," J. Neural Eng., vol. 5, pp. 114, Mar. 2008. [3] P. C. Petrantonakis and L. J. Hadjileontiadis, "A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEGBased Emotion Recognition," IEEE Trans. Inf. Technol. in Biomed., vol. 15, pp. 737746, Sep. 2011. [4] R. Vinjamuri, D. J. Weber, M. Zhi-Hong, J. L. Collinger, A. D. Degenhart, J. W. Kelly, M. L. Boninger, E. C. Tyler-Kabara, and W. Wei, "Toward Synergy-Based Brain-Machine Interfaces," IEEE Trans. Inf. Technol. Biomed., vol. 15, pp. 726736, Sep. 2011. [5] A. Kubler, V. K. Mushahwar, L. R. Hochberg, and J. P. Donoghue, "BCI meeting 2005-workshop on clinical issues and applications," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, pp. 131134, June 2006. [6] D. A. Craig and H. T. Nguyen, "Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control," in Proc. IEEE 29th Annu. Int. Conf. Eng. Med. Biol. Soc. , 2007, pp. 25442547. [7] F. Galan, M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips, and J. d. R. Millan, "A brain-actuated wheelchair: Asynchronous and noninvasive Brain-computer interfaces for continuous control of robots," Clin. Neurophysiol., vol. 119, pp. 21592169, Sep. 2008. [8] J. d. R. Millan, F. Galan, D. Vanhooydonck, E. Lew, J. Philips, and M. Nuttin, "Asynchronous non-invasive brain-actuated control of an intelligent wheelchair," in Proc. IEEE 31st Annu. Int. Conf. Eng. Med. Biol. Soc., 2009, pp. 33613364. [9] A. Kubler, F. Nijboer, J. Mellinger, T. M. Vaughan, H. Pawelzik, G. Schalk, D. J. McFarland, N. Birbaumer, and J. R. Wolpaw, "Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface," Neurol., vol. 64, pp. 17751777, May 2005. [10] G. Pfurtscheller, C. Brunner, A. Schlogl, and F. Lopes da Silva, "Mu rhythm (de) synchronization and EEG single-trial classification of
[21]
[22] [23]
[24]
[25]
[26]
[27] [28] [29]
different motor imagery tasks," NeuroImage, vol. 31, pp. 153159, May 2006. R. Leeb, D. Friedman, G. R. Müller-Putz, R. Scherer, M. Slater, and G. Pfurtscheller, "Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic," Comput. intell. and neurosci., vol. 2007, pp. 18, 2007. B. Allison and C. Neuper, "Could Anyone Use a BCI?," in BrainComputer Interfaces, Applying our Minds to Human-Computer Interaction, D. S. Tan and A. Nijholt, Eds.: Springer London, 2010, pp. 3554. M. Conson, S. Sacco, M. Sarà, F. Pistoia, D. Grossi, and L. Trojano, "Selective motor imagery defect in patients with locked-in syndrome," Neuropsychologia, vol. 46, pp. 26222628, 2008. G. E. Birch, Z. Bozorgzadeh, and S. G. Mason, "Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 10, pp. 219224, Dec. 2002. E. Curran, P. Sykacek, M. Stokes, S. J. Roberts, W. Penny, I. Johnsrude, and A. M. Owen, "Cognitive tasks for driving a braincomputer interfacing system: a pilot study," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 12, pp. 4854, Mar. 2004. R. Palaniappan, "Identifying Individuality Using Mental Task Based Brain Computer Interface," in Proc. 3rd Int. Conf. Intelligent Sensing and Information Processing, 2005, pp. 238242. F. Faradji, R. K. Ward, and G. E. Birch, "Design of a mental task-based brain-computer interface with a zero false activation rate using very few EEG electrode channels," in Proc. 4th Int. IEEE/EMBS Conf. on Neural Eng., 2009, pp. 403406. R. Chai, S. H. Ling, G. P. Hunter, and H. T. Nguyen, "Mental nonmotor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network," in Proc. 2012 Int. Joint Conf. Neural Networks (IJCNN), 2012, pp. 978984. R. Chai, S. H. Ling, G. P. Hunter, and H. T. Nguyen, "Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller," in Proc. IEEE 34th Annu. Int. Conf. Eng. Med. Biol. Soc. , 2012, pp. 52665269. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, "A review of classification algorithms for EEG-based brain–computer interfaces," J. Neural Eng., vol. 4, pp. R1R13, Mar. 2007. D. J. Krusienski, M. Grosse-Wentrup, F. Galán, D. Coyle, K. J. Miller, E. Forney, and C. W. Anderson, "Critical issues in state-of-the-art brain–computer interface signal processing," J. Neural Eng., vol. 8, p. 025002, Mar. 2011. C. Deng, H. Xiaofei, H. Jiawei, and T. S. Huang, "Graph Regularized Nonnegative Matrix Factorization for Data Representation," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, pp. 15481560, Aug. 2011. S. H. Ling, H. T. Nguyen, F. H. F. Leung, K. Y. Chan, and F. Jiang, "Intelligent fuzzy particle swarm optimization with cross-mutated operation," in Proc. 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 30093016. G. H. Klem, H. Lüders, H. Jasper, and C. Elger, "The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology," Electroenceph. Clin. Neurophysiol., vol. 52, p. 3, 1999. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis," Proc. Roy. Soc. London A, vol. 454, pp. 903995, Mar. 1998. C. Dan, L. Duan, X. Muzhou, B. Hong, and L. Xiaoli, "GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia," IEEE Trans. Inf. Technol. Biomed., vol. 14, pp. 14171427, Nov. 2010. V. Bajaj and R. B. Pachori, "Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition," IEEE Trans. Inf. Technol. Biomed., vol. 16, pp. 1135-1142, Nov. 2012. H. T. Nguyen, "Intelligent technologies for real-time biomedical engineering applications," Int. J. Automation and Control, vol. 2, Nos.2/3, pp. 274285, 2008. A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, "Epileptic Seizure Detection in EEGs Using Time-Frequency Analysis," IEEE Trans. Inf. Technol. Biomed., vol. 13, pp. 703-710, Sep. 2009.
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2< [30] A. Craig, P. Moses, Y. Tran, P. McIsaac, and L. Kirkup, "The effectiveness of a hands-free environmental control system for the profoundly disabled," Archives of physical medicine and rehabilitation, vol. 83, pp. 1455-1458, 2002. [31] B. Blankertz, G. Dornhege, S. Lemm, M. Krauledat, G. Curio, and K.R. Muller, "The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States," J. UCS, vol. 12, pp. 581607, 2006. [32] M. Grosse-Wentrup and B. Schölkopf, "A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs)," in Brain-Computer Interface Research, C. Guger, B. Z. Allison, and G. Edlinger, Eds.: Springer Berlin Heidelberg, 2013, pp. 3951. [33] D. J. McFarland and D. J. Krusienski, "BCI Signal Processing: Feature Translation," in Brain–Computer Interfaces: Principles and Practice, J. Wolpaw and E. W. Wolpaw, Eds. New York: Oxford University Press, 2012, pp. 147163. [34] M. Billinger, I. Daly, V. Kaiser, J. Jin, B. Z. Allison, G. R. Müller-Putz, and C. Brunner, "Is it significant? Guidelines for reporting BCI performance," in Towards Practical Brain-Computer Interfaces: Springer, 2013, pp. 333354. [35] V. Franc and V. Hlavac. (2004). Statistical pattern recognition toolbox for Matlab. [Online]. Available: http://cmp.felk.cvut.cz/cmp/software/stprtool/. [36] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using LDA-based algorithms," IEEE Trans. Neural Netw., vol. 14, pp. 195200, Jan. 2003. [37] A. Delorme, C. Kothe, A. Vankov, N. Bigdely-Shamlo, R. Oostenveld, T. O. Zander, and S. Makeig, "MATLAB-based tools for BCI research," in Brain-Computer Interfaces: Springer, 2010, pp. 241259. [38] N. Fatma Guler and E. D. Ubeyli, "Multiclass Support Vector Machines for EEG-Signals Classification," IEEE Trans. Inf. Technol. in Biomed., vol. 11, pp. 117126, Mar. 2007. [39] Z. Rui, G. McAllister, B. Scotney, S. McClean, and G. Houston, "Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response," IEEE Trans. Inf. Technol. in Biomed., vol. 10, pp. 458467, July 2006. [40] A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, pp. 10841093, 2007. [41] W. Ting, Y. Guo-zheng, Y. Bang-hua, and S. Hong, "EEG feature extraction based on wavelet packet decomposition for brain computer interface," Measurement, vol. 41, pp. 618625, 2008. [42] Y. Chen and M. Q. Feng, "A technique to improve the empirical mode decomposition in the Hilbert-Huang transform," Earthquake Engineering and Engineering Vibration, vol. 2, pp. 7585, 2003. [43] Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, pp. 141, 2009.
Rifai Chai (S’11) received the B. Eng. degree from Krida Wacana Christian University, Jakarta, Indonesia in 2000. From 2000 to 2010 he has worked as a Product Development Engineer; Research and Development Engineer; and Project Engineer with companies in Indonesia and Australia. Currently, he is working toward Ph.D degree in biomedical engineering with Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests on brain-computer interfaces, biomedical instrumentation, embedded system and computational intelligence using neural networks, fuzzy logic and evolutionary computation.
11
S.H. Ling (M’06-SM’12) received the BEng degree from the Department of Electrical Engineering, M.Phil. and Ph.D. degrees from the Department of Electronic and Information Engineering in the Hong Kong Polytechnic University in 1999, 2002 and 2007 respectively. Currently, he works in University of Technology, Sydney, Australia as Lecturer. He has authored and coauthored over 130 books, international journal and conference papers on computational Intelligence and its industrial applications. His current research interests include evolution computations, fuzzy logics, neural networks, hybrid systems and biomedical applications. Currently, he serves as Co-Editors-in-Chief for Journal of Intelligent Learning Systems and Applications.
Gregory P. Hunter (M’86) received the B.Eng. (Hons) degree from the University of Sydney, Australia in 1975 and the Ph.D. degree from the University of Technology, Sydney, Australia in 1998, both in electrical engineering. He has worked in the power electronics industry for most of his career both as an employee and a consultant, specialising in the design of switched mode power supplies, uninterruptible power supplies, gridconnect inverters, and motor drives using both PWM inverters and cycloconverters. From 1998 he has been a Senior Research Fellow at the University of Technology, Sydney. His current research interests include sensorless motor drives, wind turbines, electric wheelchair controllers and power electronics for implanted medical devices.
Yvonne Tran received her BSc (Hons) in Biomedical Science in 1997 and Ph.D. degree in Psychophysiology in 2001 both from the University of Technology, Sydney, Australia. In 2001, she joined the Centre of Health Technology, Faculty of Engineering and Information Technology, University of Technology, Sydney as a Postdoctoral Fellow and is currently working as a Research Associate for this centre. In 2007, she joined the Rehabilitation Studies Unit, University of Sydney, Australia as a Senior Research Officer. Her area of research include investigating neural signals for BCI use, neuropsychophysiology and cognitive associations of deafferentation in people with spinal cord injury, detection of psychophysiological signals following a fatiguing task and psychological injury following a motor vehicle accident.
Hung T. Nguyen (SM’99) is a Professor of Electrical Engineering at the University of Technology, Sydney (UTS). He is Dean of the Faculty of Engineering and Information Technology and Director of the Centre for Health Technologies. He received his PhD in 1980 from the University of Newcastle, Australia. His research interests include biomedical engineering, advanced control and artificial intelligence. He has developed biomedical devices for diabetes, disability, and cardiovascular diseases. He is a senior member of the Institute of Electrical and Electronic Engineers; and a Fellow of the Institution of Engineers, Australia, the British Computer Society and the Australian Computer Society.
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
> TITB-00323-2013.R2
TITB-00323-2013.R2< features extraction and classification algorithms [20][22]. Features extraction based on fast Fourier transform (FFT) has been used widely in the EEG-based BCI. However, the Hilbert-Huang transform (HHT) has been shown to provide a better result compared to conventional FFT to tackle nonlinear and non-stationary signals of mental tasks for EEGbased BCI [19]. For the classification algorithm, artificial neural network (ANN) is known as a non-linear classification method and has been used to handle the mental task basedBCI [6], [18], [19]. A genetic algorithm-based ANN (GAANN) has been applied to optimize the ANN training in the mental task-based BCI [18], [19]. Recently, an improved optimization technique using fuzzy particle swarm optimization with the cross-mutated operation (FPSOCM) was proposed [23]. In FPSOCM, the fuzzy inertia weight that provides nonlinearity characteristics can offer an enhanced searching quality; the cross-mutated operation can effectively handle the drawback of trapping in local optima. This paper explores further use of the FPSCOCM-ANN algorithm in BCI applications. The contributions of this paper are as follows: firstly, this paper combines the neural-network classifier with the fuzzy particle swarm optimization with cross-mutated operation (FPSOCM-ANN) for a three-class of mental task-based BCI classification. The features extraction method is based on the Hilbert-Huang transform (HHT). For comparison purposes, different classifiers and feature extractors are included to find the best algorithms with the highest accuracy. The non-motor imagery mental tasks used are letter composing, arithmetic and figure Rubik’s cube rolling forward, which can be mapped for three wheelchair commands: left, right and forward. An additional eyes closed task is recorded as well for testing an on-off command. Secondly, most of the results of the mental task-based BCI were from experiments on able-bodied subjects only. This paper includes five able-bodied subjects and five patients with tetraplegia. Thirdly, different timewindows of data are investigated to find the best data windowing with an improved result of classification accuracy. Fourthly, for practical reasons, results of combinations of two channels classification are presented to find the two channels of EEG signals suitable for mental task-based BCI. The structure of this paper is as follows: section II covers the methodology: general structure, data collection, feature extraction methods and classification algorithms. Section III describes results and discussion, followed by section IV for the conclusion.
2 training and classification tasks. The outputs classification can be mapped to the three wheelchair steering commands.
Fig. 1. Components of mental task-based BCI
B. Experimental Data Collection The Human Research Ethics Committee of University of Technology, Sydney approved this study. A total of ten participants were involved: five able-bodied subjects (S1-S5) aged between 25 and 35 years; five patients with tetraplegia (T1-T5) aged between 45 and 80 years who suffer a high-level of SCI in the cervical area at level C3, C4, C5 and C6 with the details as shown in Table I and Fig. 2. TABLE I
ABLE BODIED SUBJECTS AND PATIENTS WITH TETRAPLEGIA DETAILS Participants S1, S2, S3,S4, S5 T1 T2 T3 T4 T5
Age 25-35 80 59 50 55 45
Description Able-bodied subjects Tetraplegia, cervical SCI at C3- C4 Tetraplegia, cervical SCI at C5- C6 Tetraplegia, cervical SCI at C3- C4 Tetraplegia, cervical SCI at C3- C4 Tetraplegia, cervical SCI at C5- C6
A commercial 32-channels EEG system, from Compumedics was used with the sampling rate of the system at 256 Hz. This study used 6 channels with the electrodes positioned at locations C3, C4, P3, P4, O1 and O2. The left earlobe (A1) was used as the reference and the GND electrode was attached to the right earlobe (A2) as shown in Fig 2. This configuration refers to the standard 10-20 electrode montage system [24]. During the experiment, EEG gel was applied; the electrode contact impedance was measured and maintained below 5kΩ. Participants were asked to keep eye blinks, and unnecessary movements to a minimum during the experiment. Data with strong presence of artifacts was discarded.
II. METHODOLOGY A. General Structure Fig. 1 illustrates the basic components for the non-motor imagery mental task-based BCI for this paper. This starts from data collection by using EEG, followed by a signal preprocessing module: window segmentation and digital filters. Next, a features extraction module transforms the signals into useful features. The features are processed into a classification algorithm, in this case an ANN which includes optimization,
Fig. 2. Experiment setup of EEG-BCI with patients with tetraplegia
As an offline study, a proper standard protocol is needed to ensure all participants perform tasks correctly. A total of three non-motor imagery mental tasks were used including mental letter composing, arithmetic and a figure of Rubik’s cube rolling. Prior to the beginning of the session, participants were shown a video as a guidance to perform mental tasks for the
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2
pcm then Perform cross-mutated operation output (ƒ(X(t)))
Time
Fig. 6. EMD process of eyes closed signal at 1s time-window
end return the best particle, g
120
end Fig. 4. The FPSOCM procedure.
(9)
where fitness denotes the fitness value and err is the mean square error (MSE). It can be seen from the defined formula that a larger fitness value implies a smaller MSE. For the performance measurement, classification accuracy was used in this paper as evaluation criteria. This is a widely used evaluation criteria in BCI. Comparisons of classifier are given including GA-ANN, support vector machine (SVM), linear discriminate analysis (LDA) and linear perceptron are given in the next section. III. RESULTS AND DISCUSSIONS A. Testing the HHT as the Features Extractor Initial testing was conducted for the HHT with a known signal of four combination sinusoid signals as defined in (6). The result is shown in Fig.5, EMD as the first step of HHT for this signal was composed correctly of five IMFs with the first four IMFs representing the four defined sinusoidal signals at different frequencies and the fifth IMF representing the residue. It has been known that during the eyes closed task, there is a dominant feature in the alpha band of EEG (8-13Hz) [30]. This unique feature can be used for further testing to ensure
Frequency (Hz)
100
The objective of the FPSOCM-ANN training is to minimize the fitness (cost) values interactively with the fitness function: fitness 1 / (1 err )
Time
80 60 40
20 Alpha 0
50
100 150 200 250 Count (1s =256 count) Fig. 7. HHT spectrum of eyes closed signal at a 1s time-window
The segmented EEG feature data (eyes closed task) was processed and converted into a series of IMFs and residue in the EMD process as shown in Fig.6. This was followed by applying the HT method to the IMFs resulting in the amplitude and instantaneous frequency as functions of time. The plotting of the HHT spectrum in Fig.7 for the eyes closed task shows a clear dominant feature of the instantaneous frequency of the alpha EEG band (8-13Hz). This proves the features extraction method has correctly converted EEG data for the eyes closed task into the proper feature. B. Classification using FPSOCM –ANN at different timewindows and Comparison with GA-ANN The training of the neural network was repeated 10 times for each different hidden neuron; thus, the reported accuracies were the mean value of 10 results of accuracies. The number of hidden neurons was varied from 4 to 30 units to obtain the
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2< bodied subjects (S1-S5) and five patients with tetraplegia (T1T5). Between 1s to 6s time-windows, the average accuracies for able-bodied subjects were increased in each higher timewindow between 67.3±5.7% and 76.5±7.9% using GA-ANN. These were improved using FPSOCM-ANN compared to GAANN with improved accuracies between 72.0±3.9% and 83.9±6.7%. Patients with tetraplegia resulted in accuracies of between 59.7±1.5% and 74.4±4.2% using GA-ANN and improved accuracies based on FPSOCM-ANN with an average accuracy of between 63.0±1.0% and 81.8±3.8%. The overall mean accuracy of three mental tasks classification at between 1s to 6s time-windows for both groups were between 63.5±5.6% and 75.5±6.1% with GA-ANN and improved accuracies between 67.5±5.5% and 82.9±5.2% were found with FPSOCM-ANN. The highest overall accuracy was reached at 7s timewindow with improved accuracies for both groups compared to previous time-windows. In details, the average accuracy of five able-bodied subjects was 79.0±8.6% using GA-ANN method and improved accuracy at 85.5±5.5% using FPSOCMANN. On five patients with tetraplegia, the average accuracy resulted at 75.9±5.3% using GA-ANN and this accuracy was increased using FPSOCM-ANN with an average accuracy at 83.3±6.0%. The overall average accuracy of three mental tasks classification for two groups combined at a 7s time-window was 77.4±6.9% using GA-ANN and improved accuracy at 84.4±5.5% using FPSOCM-ANN. Compared to the previous time-window, the overall accuracies at between 8s and 10s time windows were much more in a steady value. The average accuracies for ablebodied subjects were between 77.2±7.6% and 78.0±6.9% with GA-ANN. These were improved using FPSOCM-ANN compared to GA-ANN with accuracies of around 84.6±5.3%. For patients with tetraplegia, accuracies using GA-ANN were between 75.4±4.8 % and 76.9±6.4 and improved accuracies based on FPSOCM-ANN with an average accuracy of around 83.4±5.4%. The overall accuracies of three mental tasks classification at between 8s to 10s time-windows for both groups were between 76.5±6.7% and 77.1±6.6% with GAANN and improved accuracies of around 84.0±5.1% using FPSOCM-ANN. In general, for three mental task classifications, the results show an overall improved accuracy for the classification algorithm using FPSOCM-ANN compared to GA-ANN across both groups and in different time-windows. For comparison between able-bodied subjects and patients with tetraplegia, the patients’ group provided lower classification accuracy. However, the accuracy was improved by increasing the duration of the time-window with at best achieved accuracy for a 7s time-window of 77.4% based on GA-ANN and improved accuracy of 84.4% using FPSOCM-ANN. From this experiment, patients with tetraplegia tend to have better classification accuracy when increasing the time-window which means they need more time to perform the mental tasks due with their disability issue. This can be found using FPSOCM-ANN, where for a 1s time-window, patients with
6 tetraplegia achieve an accuracy of 63% compared with 72% for able-bodied subjects. The patients group had accuracy around 9% lower than the able-bodied group. At 7s timewindow, the accuracy difference between both groups was much smaller at only 2.2% with an accuracy for patients with tetraplegia of 83.3% and able-bodied subject of 85.5%. C. Further Comparison classifiers and features extractors Further comparisons are carried out between different classifiers and feature extractors as shown in Table III using the 7s time-window of data as the best time window. Due to the output classifier using three mental tasks, instead of using a binary classifier, the comparison classifiers need a multiclass model. These classifiers are: multi-class linear perceptron classifier with the Kesler’s construction [35]; multi-class linear discriminant analysis (LDA) with the fisher kernel [36], [37]; and multi-class support vector machine (SVM) using BSVM (B refers to the added bias) [35], [38]. For the feature extraction methods comparison, FFT [18] and wavelet [39] methods are presented. The discrete wavelet transform (DWT) with Daubechies type as the mother wavelet in a 5 level decomposition was used in accordance with previous BCI-EEG analyses [40], [41]. There are improvements on the original HHT especially with the EMD analysis available [42], [43]. The HHT using the ensemble empirical mode decomposition (EEMD) [43] was also included. The result shows the multi-class linear perceptron classifier provided an overall mean accuracy of: 63.7±2.5% using the FFT; 67.1±2.8% using the wavelet; 71±4.5% using the HHTEMD; and 71.7±3.8 % using the HHT-EEMD. The accuracies are slightly improved across different feature extractors when using the multi-class LDA classifier with the overall mean accuracy of: 64.8±3.3% using the FFT; 68.1±2.7% using the wavelet; 72.1±4.5% using the HHT-EMD; and 72.5±4.4% using the HHT-EEMD. The multi-class SVM classifier provided improved results across different features extractors to the previous two methods with overall mean accuracy of: 66.9±2.7% using FFT; 70.6±3.2% using wavelet; 77.0±6.6% using HHT-EMD; 63.7±2.5%; and 76.8±5.6% using HHTEEMD. For the GA-ANN classifier, the accuracy is further improved compare to previous classifiers with the overall accuracy of: 67.8±3.4% using FFT; 71.8±3.3% using wavelet; 77.4±6.9% using HHT-EMD; and 77.9±6.7% using the HHTEEMD. The FPSOCM-ANN classifier provided the most improvement with the overall mean accuracy of: 70.7±2.9% using FFT; 75.7±4.1% using wavelet; 84.4±5.5% using HHTEMD; 84.5±4.9% using HHT-EEMD. The neural networks classifiers (GA-ANN and FPSOCMANN) with the optimization methods in this study provide better results compared to the linear perceptron, LDA and SVM methods. The FPSOCM-ANN provided the best classifier among other classifiers. The HHT as the feature extractor has higher accuracy compared to FFT and wavelet methods. In the FPSOCM-ANN, it can also be seen that there is a comparable accuracy between HHT-EMD and HHTEEMD in this study, thus both methods can be used.
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2
TITB-00323-2013.R2
TITB-00323-2013.R2
TITB-00323-2013.R2< signal. Further HHT testing for the eyes closed EEG signal shows a correct dominant alpha (8-13Hz) wave with high classification accuracy for both groups of participants. Results for three mental tasks classification show improved accuracy of FPSOCM-ANN compared to GA-ANN and other classifiers in multi-class mode (SVM, LDA and linear perceptron) across both groups and different time-windows. Although the patients group has lower classification accuracy, this is improved by increasing the time windows with the best classification accuracy achieved at a 7s time-window. For practical use of a BCI, combinations of two channels EEG have also been presented with the variation result of the best two channels in each subject. For overall generalization of both groups, O1and C4 are the best two channels, followed by the second best at P3 and O2, and the third best at C3 and O2 channels. For each correctly classified mental task, the arithmetic task has the highest rate of classification, followed by the rolling cube task and letter composing task. In the online application as future work, these three non-motor imagery mental tasks can be directly mapped into three wheelchair commands. For example the mental letter composing task is used for left command; arithmetic mental task for right command; and imagining a rolling cube for forward command. An additional eyes closed task can be used for on-off command. The users perform the imagery mental task at will which are initiated by users’ themselves (selfpaced) for the wheelchair control application.
10
[11]
[12]
[13] [14]
[15]
[16] [17]
[18]
[19]
REFERENCES [20] [1]
J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, "Brain-computer interfaces for communication and control," Clin. Neurophysiol., vol. 113, pp. 767791, June 2002. [2] G. Schalk, "Brain–computer symbiosis," J. Neural Eng., vol. 5, pp. 114, Mar. 2008. [3] P. C. Petrantonakis and L. J. Hadjileontiadis, "A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEGBased Emotion Recognition," IEEE Trans. Inf. Technol. in Biomed., vol. 15, pp. 737746, Sep. 2011. [4] R. Vinjamuri, D. J. Weber, M. Zhi-Hong, J. L. Collinger, A. D. Degenhart, J. W. Kelly, M. L. Boninger, E. C. Tyler-Kabara, and W. Wei, "Toward Synergy-Based Brain-Machine Interfaces," IEEE Trans. Inf. Technol. Biomed., vol. 15, pp. 726736, Sep. 2011. [5] A. Kubler, V. K. Mushahwar, L. R. Hochberg, and J. P. Donoghue, "BCI meeting 2005-workshop on clinical issues and applications," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 14, pp. 131134, June 2006. [6] D. A. Craig and H. T. Nguyen, "Adaptive EEG Thought Pattern Classifier for Advanced Wheelchair Control," in Proc. IEEE 29th Annu. Int. Conf. Eng. Med. Biol. Soc. , 2007, pp. 25442547. [7] F. Galan, M. Nuttin, E. Lew, P. W. Ferrez, G. Vanacker, J. Philips, and J. d. R. Millan, "A brain-actuated wheelchair: Asynchronous and noninvasive Brain-computer interfaces for continuous control of robots," Clin. Neurophysiol., vol. 119, pp. 21592169, Sep. 2008. [8] J. d. R. Millan, F. Galan, D. Vanhooydonck, E. Lew, J. Philips, and M. Nuttin, "Asynchronous non-invasive brain-actuated control of an intelligent wheelchair," in Proc. IEEE 31st Annu. Int. Conf. Eng. Med. Biol. Soc., 2009, pp. 33613364. [9] A. Kubler, F. Nijboer, J. Mellinger, T. M. Vaughan, H. Pawelzik, G. Schalk, D. J. McFarland, N. Birbaumer, and J. R. Wolpaw, "Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface," Neurol., vol. 64, pp. 17751777, May 2005. [10] G. Pfurtscheller, C. Brunner, A. Schlogl, and F. Lopes da Silva, "Mu rhythm (de) synchronization and EEG single-trial classification of
[21]
[22] [23]
[24]
[25]
[26]
[27] [28] [29]
different motor imagery tasks," NeuroImage, vol. 31, pp. 153159, May 2006. R. Leeb, D. Friedman, G. R. Müller-Putz, R. Scherer, M. Slater, and G. Pfurtscheller, "Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic," Comput. intell. and neurosci., vol. 2007, pp. 18, 2007. B. Allison and C. Neuper, "Could Anyone Use a BCI?," in BrainComputer Interfaces, Applying our Minds to Human-Computer Interaction, D. S. Tan and A. Nijholt, Eds.: Springer London, 2010, pp. 3554. M. Conson, S. Sacco, M. Sarà, F. Pistoia, D. Grossi, and L. Trojano, "Selective motor imagery defect in patients with locked-in syndrome," Neuropsychologia, vol. 46, pp. 26222628, 2008. G. E. Birch, Z. Bozorgzadeh, and S. G. Mason, "Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 10, pp. 219224, Dec. 2002. E. Curran, P. Sykacek, M. Stokes, S. J. Roberts, W. Penny, I. Johnsrude, and A. M. Owen, "Cognitive tasks for driving a braincomputer interfacing system: a pilot study," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 12, pp. 4854, Mar. 2004. R. Palaniappan, "Identifying Individuality Using Mental Task Based Brain Computer Interface," in Proc. 3rd Int. Conf. Intelligent Sensing and Information Processing, 2005, pp. 238242. F. Faradji, R. K. Ward, and G. E. Birch, "Design of a mental task-based brain-computer interface with a zero false activation rate using very few EEG electrode channels," in Proc. 4th Int. IEEE/EMBS Conf. on Neural Eng., 2009, pp. 403406. R. Chai, S. H. Ling, G. P. Hunter, and H. T. Nguyen, "Mental nonmotor imagery tasks classifications of brain computer interface for wheelchair commands using genetic algorithm-based neural network," in Proc. 2012 Int. Joint Conf. Neural Networks (IJCNN), 2012, pp. 978984. R. Chai, S. H. Ling, G. P. Hunter, and H. T. Nguyen, "Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller," in Proc. IEEE 34th Annu. Int. Conf. Eng. Med. Biol. Soc. , 2012, pp. 52665269. F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, and B. Arnaldi, "A review of classification algorithms for EEG-based brain–computer interfaces," J. Neural Eng., vol. 4, pp. R1R13, Mar. 2007. D. J. Krusienski, M. Grosse-Wentrup, F. Galán, D. Coyle, K. J. Miller, E. Forney, and C. W. Anderson, "Critical issues in state-of-the-art brain–computer interface signal processing," J. Neural Eng., vol. 8, p. 025002, Mar. 2011. C. Deng, H. Xiaofei, H. Jiawei, and T. S. Huang, "Graph Regularized Nonnegative Matrix Factorization for Data Representation," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, pp. 15481560, Aug. 2011. S. H. Ling, H. T. Nguyen, F. H. F. Leung, K. Y. Chan, and F. Jiang, "Intelligent fuzzy particle swarm optimization with cross-mutated operation," in Proc. 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 30093016. G. H. Klem, H. Lüders, H. Jasper, and C. Elger, "The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology," Electroenceph. Clin. Neurophysiol., vol. 52, p. 3, 1999. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, "The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis," Proc. Roy. Soc. London A, vol. 454, pp. 903995, Mar. 1998. C. Dan, L. Duan, X. Muzhou, B. Hong, and L. Xiaoli, "GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia," IEEE Trans. Inf. Technol. Biomed., vol. 14, pp. 14171427, Nov. 2010. V. Bajaj and R. B. Pachori, "Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition," IEEE Trans. Inf. Technol. Biomed., vol. 16, pp. 1135-1142, Nov. 2012. H. T. Nguyen, "Intelligent technologies for real-time biomedical engineering applications," Int. J. Automation and Control, vol. 2, Nos.2/3, pp. 274285, 2008. A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, "Epileptic Seizure Detection in EEGs Using Time-Frequency Analysis," IEEE Trans. Inf. Technol. Biomed., vol. 13, pp. 703-710, Sep. 2009.
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/JBHI.2013.2295006, IEEE Journal of Biomedical and Health Informatics
> TITB-00323-2013.R2< [30] A. Craig, P. Moses, Y. Tran, P. McIsaac, and L. Kirkup, "The effectiveness of a hands-free environmental control system for the profoundly disabled," Archives of physical medicine and rehabilitation, vol. 83, pp. 1455-1458, 2002. [31] B. Blankertz, G. Dornhege, S. Lemm, M. Krauledat, G. Curio, and K.R. Muller, "The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States," J. UCS, vol. 12, pp. 581607, 2006. [32] M. Grosse-Wentrup and B. Schölkopf, "A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs)," in Brain-Computer Interface Research, C. Guger, B. Z. Allison, and G. Edlinger, Eds.: Springer Berlin Heidelberg, 2013, pp. 3951. [33] D. J. McFarland and D. J. Krusienski, "BCI Signal Processing: Feature Translation," in Brain–Computer Interfaces: Principles and Practice, J. Wolpaw and E. W. Wolpaw, Eds. New York: Oxford University Press, 2012, pp. 147163. [34] M. Billinger, I. Daly, V. Kaiser, J. Jin, B. Z. Allison, G. R. Müller-Putz, and C. Brunner, "Is it significant? Guidelines for reporting BCI performance," in Towards Practical Brain-Computer Interfaces: Springer, 2013, pp. 333354. [35] V. Franc and V. Hlavac. (2004). Statistical pattern recognition toolbox for Matlab. [Online]. Available: http://cmp.felk.cvut.cz/cmp/software/stprtool/. [36] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using LDA-based algorithms," IEEE Trans. Neural Netw., vol. 14, pp. 195200, Jan. 2003. [37] A. Delorme, C. Kothe, A. Vankov, N. Bigdely-Shamlo, R. Oostenveld, T. O. Zander, and S. Makeig, "MATLAB-based tools for BCI research," in Brain-Computer Interfaces: Springer, 2010, pp. 241259. [38] N. Fatma Guler and E. D. Ubeyli, "Multiclass Support Vector Machines for EEG-Signals Classification," IEEE Trans. Inf. Technol. in Biomed., vol. 11, pp. 117126, Mar. 2007. [39] Z. Rui, G. McAllister, B. Scotney, S. McClean, and G. Houston, "Combining Wavelet Analysis and Bayesian Networks for the Classification of Auditory Brainstem Response," IEEE Trans. Inf. Technol. in Biomed., vol. 10, pp. 458467, July 2006. [40] A. Subasi, "EEG signal classification using wavelet feature extraction and a mixture of expert model," Expert Systems with Applications, vol. 32, pp. 10841093, 2007. [41] W. Ting, Y. Guo-zheng, Y. Bang-hua, and S. Hong, "EEG feature extraction based on wavelet packet decomposition for brain computer interface," Measurement, vol. 41, pp. 618625, 2008. [42] Y. Chen and M. Q. Feng, "A technique to improve the empirical mode decomposition in the Hilbert-Huang transform," Earthquake Engineering and Engineering Vibration, vol. 2, pp. 7585, 2003. [43] Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, pp. 141, 2009.
Rifai Chai (S’11) received the B. Eng. degree from Krida Wacana Christian University, Jakarta, Indonesia in 2000. From 2000 to 2010 he has worked as a Product Development Engineer; Research and Development Engineer; and Project Engineer with companies in Indonesia and Australia. Currently, he is working toward Ph.D degree in biomedical engineering with Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests on brain-computer interfaces, biomedical instrumentation, embedded system and computational intelligence using neural networks, fuzzy logic and evolutionary computation.
11
S.H. Ling (M’06-SM’12) received the BEng degree from the Department of Electrical Engineering, M.Phil. and Ph.D. degrees from the Department of Electronic and Information Engineering in the Hong Kong Polytechnic University in 1999, 2002 and 2007 respectively. Currently, he works in University of Technology, Sydney, Australia as Lecturer. He has authored and coauthored over 130 books, international journal and conference papers on computational Intelligence and its industrial applications. His current research interests include evolution computations, fuzzy logics, neural networks, hybrid systems and biomedical applications. Currently, he serves as Co-Editors-in-Chief for Journal of Intelligent Learning Systems and Applications.
Gregory P. Hunter (M’86) received the B.Eng. (Hons) degree from the University of Sydney, Australia in 1975 and the Ph.D. degree from the University of Technology, Sydney, Australia in 1998, both in electrical engineering. He has worked in the power electronics industry for most of his career both as an employee and a consultant, specialising in the design of switched mode power supplies, uninterruptible power supplies, gridconnect inverters, and motor drives using both PWM inverters and cycloconverters. From 1998 he has been a Senior Research Fellow at the University of Technology, Sydney. His current research interests include sensorless motor drives, wind turbines, electric wheelchair controllers and power electronics for implanted medical devices.
Yvonne Tran received her BSc (Hons) in Biomedical Science in 1997 and Ph.D. degree in Psychophysiology in 2001 both from the University of Technology, Sydney, Australia. In 2001, she joined the Centre of Health Technology, Faculty of Engineering and Information Technology, University of Technology, Sydney as a Postdoctoral Fellow and is currently working as a Research Associate for this centre. In 2007, she joined the Rehabilitation Studies Unit, University of Sydney, Australia as a Senior Research Officer. Her area of research include investigating neural signals for BCI use, neuropsychophysiology and cognitive associations of deafferentation in people with spinal cord injury, detection of psychophysiological signals following a fatiguing task and psychological injury following a motor vehicle accident.
Hung T. Nguyen (SM’99) is a Professor of Electrical Engineering at the University of Technology, Sydney (UTS). He is Dean of the Faculty of Engineering and Information Technology and Director of the Centre for Health Technologies. He received his PhD in 1980 from the University of Newcastle, Australia. His research interests include biomedical engineering, advanced control and artificial intelligence. He has developed biomedical devices for diabetes, disability, and cardiovascular diseases. He is a senior member of the Institute of Electrical and Electronic Engineers; and a Fellow of the Institution of Engineers, Australia, the British Computer Society and the Australian Computer Society.
Copyright (c) 2013 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing [email protected].
