290
Electroencephalography and Clinical Neurophysiology, 1978, 4 5 : 2 9 0 - - 2 9 4 © Elsevier/North-Holland Scientific Publishers, Ltd.
Laboratory note EEG FEEDBACK TRAINING: ENHANCEMENT OF SOMATOSENSORY CORTICAL ACTIVITY * WILLIAM N. KUHLMAN
Neuropsychology Laboratory, Veterans Administration Hospital, West Haven, Conn. 06516 and Departments of Psychology and Neurology, Yale University, New Haven, Conn. 06520 (U.S.A.) (Accepted for publication: February 14, 1978)
Early studies of EEG feedback training reported that human subjects can alternately enhance and suppress spontaneous rhythmic activity for short periods of time when they are provided with immediate information, or feedback, signalling its occurrence (Brown 1970; Nowlis and Kamiya 1970; Beatty et al. 1974). Although current clinical applications of EEG feedback training assume that training may produce sustained modification in neural activity (Sterman et al. 1974; Kaplan 1975; Wyler et al. 1976), there are relatively few systematic data concerning the effects of unidirectional, long-term feedback training on identified patterns of the human EEG. Operant conditioning of a spontaneous EEG rhythm recorded from somatosensory cortex in the cat, the sensorimotor rhythm or SMR (Wyrwicka and Sterman 1968; Chase and Harper 1971) has often served as an animal model for EEG feedback training in humans (Sterman et al. 1974; Finley et al. 1975; Lubar and Bahler 1976). Recent studies have shown that the human mu rhythm is analogous to the feline SMR in terms of cortical topography, relationship to behavior, and reactivity to sensory stimulation (Chase 1974; Kuhlman 1978a). Before investigating clinical applications of EEG feedback training, I therefore investigated whether long-term enhancement of mu rhythm activity was possible across 50 sessions of EEG feedback training.
Method Five volunteer subjects (4 male, 1 female; 18--36 years), in good health with no history of neurological disease, were comfortably seated in a moderately illuminated recording room and were monitored by * Supported by the Medical Research Serivce of the Veterans Administration, National Institute of Mental Health Grant MH-05286, and by a predoctoral training grant from the Epilepsy Foundation of America.
closed-circuit television. All had previously served in EEG studies and were experienced in maintaining low levels of muscle tension. Bipolar scalp recordings were derived from pairs of disc electrodes placed over somatosensory cortex and occipito-parietal cortex of the left hemisphere (Fig. 1A). Spontaneous EEG activity was continuously recorded on an inkwriter and concurrently on an FM tape recorder for subsequent power spectral analysis on a PDP-12 computer. In an initial session, subjects were tested for the presence of mu activity by the method of Kuhlman (1978a), and control data were collected prior to experimental training. Briefly, 8 channels of EEG were recorded from closely spaced bipolar chains of peri-Rolandic and occipital electrodes. Mu activity was identified as a dominant rhythm which is maximal over somatosensory cortex at rest, is attenuated by voluntary movement or somatosensory stimulation, but is minimally affected by visual stimulation (Fig. 1B; Chatrian 1976 ; Kuhlman 1978a). These spatial and functional characteristics, which are also the characteristics of the feline SMR (Roth et al. 1967; Howe and Sterman 1972; Rougeul et al. 1972; Chase 1974), distinguish mu activity from occipital alpha activity although both rhythms fall within the nominal alpha frequency band (Chatrian 1976; Kuhlman 1978a). Mu rhythm activity, with dominant frequencies of 9--11 c/sec, was identified in 3 subjects, and these subjects were subsequently given feedback for 9--11 c/sec activity. Although previous investigators have assumed that 12--14 c/sec activity in the human EEG is equivalent to the feline SMR (Sterman et al. 1974; Finley et al. 1975; Lubar and Bahler 1976), topographical analysis showed that this activity represented a desynchronized, low voltage fast (LVF) pattern which did not show the spatial or functional characteristics of the SMR or mu rhythm (Fig. 1). For purposes of comparison to feedback training of rhythmic mu activity and to investigate whether LVF activity could be enhanced, the remaining two subjects were given feedback for 12--14 c/sec activity.
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II Fig. 1. A: e x a m p l e s o f d i s t i n c t i v e m u r h y t h m activity (left) a n d d e s y n c h r o n i z e d low voltage, fast activity (right) in q u i e t alert subjects, r e c o r d e d f r o m c e n t r a l a n d o c c i p i t a l scalp e l e c t r o d e s s h o w n at left in r e l a t i o n to t h e 1 0 - - 2 0 s y s t e m o f e l e c t r o d e p l a c e m e n t . B: average p r e t r a i n i n g p o w e r s p e c t r a l d a t a for all s u b j e c t s o f t h e t w o t r a i n i n g groups. Mu activity ( 9 - - 1 1 c/sec) was m a x i m a l at rest, was a t t e n u a t e d d u r i n g self-paced m o v e m e n t o f t h e c o n t r a lateral h a n d b u t was n o t a f f e c t e d b y o p e n i n g or closing t h e eyes. T h e 1 2 - - 1 4 c/sec c o m p o n e n t o f low voltage, fast activity did n o t s h o w t h e s e f u n c t i o n a l characteristics.
E a c h s u b j e c t received 50 2 0 - m i n t r a i n i n g sessions. T w o sessions were c o n d u c t e d p e r day, 2--3 days p e r w e e k , over a p e r i o d o f 2--3 m o n t h s . E E G activity f r o m t h e c e n t r a l e l e c t r o d e s was first passed t h r o u g h a n a l o g filters s e t t o a t t e n u a t e activity o u t s i d e t h e freq u e n c y range for f e e d b a c k . Pass b a n d s w e r e : 7 - - 1 3 Hz for t h e 9 - - 1 1 c/sec t r a i n i n g group, a n d 1 0 - - 1 6 Hz for t h e 1 2 - - 1 4 c/sec t r a i n i n g g r o u p ; a t t e n u a t i o n was 24 d B / o c t a v e . O u t p u t was essentially flat w i t h i n t h e f r e q u e n c y range o f f e e d b a c k . F i l t e r e d E E G was t h e n fed t o a digital d i s c r i m i n a t o r circuit w h i c h m e a s u r e d t h e p e r i o d a n d a m p l i t u d e o f successive cycles o f activity a n d a u t o m a t i c a l l y c o n t r o l l e d f e e d b a c k p r e s e n t a t i o n 1. F o r t h e m u r h y t h m g r o u p , criteria weep 5 suc1 P e r i o d d e t e c t i o n was b y zero-crossing analysis. A m o r e d e t a i l e d d e s c r i p t i o n o f this circuit is available from the author.
cessive cycles w i t h i n t h e 9 - - 1 1 c/sec range h a v i n g a m i n i m u m a m p l i t u d e o f 10/~V. Less s t r i n g e n t criteria for L V F activity were necessary due to t h e low amplit u d e a n d s h o r t d u r a t i o n c h a r a c t e r i s t i c of this activity. Criteria were t w o successive cycles w i t h i n t h e 1 2 - - 1 4 c/sec range, w i t h an a m p l i t u d e greater t h a n 2 ~tV. W h e n criteria were m e t , a small w h i t e light in t h e r e c o r d i n g r o o m was i l l u m i n a t e d a n d r e m a i n e d o n as l o n g as specified activity was p r e s e n t . B e l o w t h e feedb a c k light, a digital c o u n t e r a c c u m u l a t e d t h e n u m b e r o f s e c o n d s t h e light was o n d u r i n g t h e t r a i n i n g session. S u b j e c t s were i n s t r u c t e d t o a t t e m p t t o k e e p t h e l i g h t o n as m u c h as possible a n d t o a d v a n c e t h e c o u n t e r t o a n u m b e r h i g h e r t h a n d u r i n g p r e c e d i n g sessions. E x c e p t as n o t e d later, n o t a n g i b l e r e w a r d was c o n t i n g e n t u p o n p e r f o r m a n c e , a l t h o u g h all s u b j e c t s were paid for p a r t i c i p a t i n g . P o w e r spectral analysis o f E E G a c t i v i t y r e c o r d e d
292
W.N. K U H L M A N
during each session, i n d e p e n d e n t of selective filtering by the feedback apparatus, provided quantitative measures of E E G change. Before input to the computer, E E G signals were filtered at 3 Hz (high pass) and 40 Hz (low pass) to eliminate artifactual signals. Power spectra were calculated for individual 4-sec epochs of activity digitized at a sample rate o f 256 points per epoch. An average of 128 4-sec spectra, sequentially sampled from the middle of a session, provided a representative p o w e r s p e c t r u m for each session.
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F e e d b a c k training led to reliable increases in mu r h y t h m (Fig. 2). Since current clinical applications of E E G feedback imply a cumulative effect of long-term training ( S t e r m a n et al. 1974; Finley et al. 1975; Kaplan 1975), linear regression analyses (Hays 1963} with a d o p t e d c o n f i d e n c e levels of P < 0.01 ( d f = 1, 48) were c o n d u c t e d on data f r o m individual subjects to d e t e r m i n e if a significant p r o p o r t i o n of the total variance could be a c c o u n t e d for by a linear f u n c t i o n of training experience. The regression analyses s h o w e d significant e n h a n c e m e n t of 9--11 c/sec activity in each subject of the m u r h y t h m group across the 50 sessions (F > 10.4). The average increase in m u activity w a s 143% as derived f r o m the regression equations. Activity e x h i b i t e d in the first 5 training sessions was n o t significantly different f r o m eyesopen and eyes-closed resting values o b t a i n e d prior to training. The increase in m u activity was n o t secondary to a non-specific increase in all activity across the f r e q u e n c y s p e c t r u m since the p r o p o r t i o n o f p o w e r in the 9--11 c/sec range relative to total p o w e r from 3 to 30 c/sec also significantly increased ( F > 8.28). Central 12--14 clsec activity did n o t reliably change, nor did 9--11 c/sec activity r e c o r d e d f r o m the occipital electrodes significantly vary as a f u n c t i o n of training. Analysis of the peak f r e q u e n c y of mu activity across sessions, i n d e p e n d e n t of variations in power, s h o w e d that the increase in 9--11 c/sec activity did not arise f r o m a shift in mu frequency. In contrast to these results, no e n h a n c e m e n t was seen in the E E G spectra o f subjects given feedback training for 12--14 c/sec L V F activity (Fig. 2). Absolute and logarithmic transforms o f p o w e r in the 12--14 c/sec range did n o t significantly increase. This activity r e m a i n e d desynchronized, low in voltage, and a r h y t h m did n o t develop with training. Since p o w e r spectral analysis is m a x i m a l l y sensitive to periodic signals, a wave-count analysis was c o n d u c t e d by playing tape-recorded EEG signals through the feedback d e t e c t i o n apparatus set at m i n i m u m threshold (1 cycle onset, 1 cycle offset, 1 p V a m p l i t u d e threshold). This analysis also showed no increase in 12--14
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Fig. 2. Upper: examples of p o w e r spectra of EEG activity recorded f r o m the central electrodes in individual subjects given feedback training to enhance 9--11 c/sec mu r h y t h m activity (left) or 12--14 c/sec low voltage fast ( L V F ) activity right. The spectra (0.25 c/sec resolution) are p l o t t e d across 50 20-min training sessions. A c t i v i t y below 3 c/sec was filtered out; p o w e r in arbitrary units. The apparent decrease in low f r e q u e n c y activity in the L V F subject's spectra (scaled higher for visualization of 12--14 c/sec activity) was n o t seen in o t h e r subjects. Lower: average p o w e r spectral data during training for all subjects of the training groups. Three f r e q u e n c y bands of interest are plotted as a f u n c t i o n of 10 blocks of 5 sessions each. All subjects given feedback training for 9--11 c/sec mu activity (left) showed increases in this activity. The increase on overall p o w e r (3--30 c/sec) is due to increased m u activity; 12--14 c/sec activity did n o t significantly change. In subjects given feedback training for 12--14 c/sec low voltage fast activity (right) no e n h a n c e m e n t was seen in any frequency band.
c/sec activity across sessions. To investigate the possibility that lack of m o t i v a t i o n c o n t r i b u t e d to the failure of subjects to increase 12--14 c/sec activity, one subject was given an additional 10 sessions of training in which she was paid a m o n e t a r y reward for each second of 12--14 c/sec activity. However,
FEEDBACK T R A I N I N G OF MU RHYTHM 12--14 c/sec activity did not increase and was not significantly different from the previous 50 sessions. In the power spectra of subjects given feedback for LVF activity, 9--11 c/sec activity did not significantly change, indicating that EEG activity in this frequency range does not automatically increase as a function of time in the experimental situation. In a subsequent study (Kuhlman 1978b), mu rhythm activity did not spontaneously increase when subjects were given feedback for other EEG patterns.
Discussion The results of this study show that enhancement ~f at least one clearly defined human EEG rhythm is possible across long-term feedback training and lend further support to the analogy between the human mu rhythm and the feline SMR. However, this does not imply that all forms of human cortical activity may be similarly modified. The 12--14 c/sec c o m p o n e n t of L V F activity could not be enhanced with training, consistent with previous reports of difficulty in effecting selective increases in activity not initially present as an identifiable entity in the EEG (Kaplan 1975; Kuhlman and Klieger 1975; Wyler et al. 1977). Thus, it would appear that the occurrence of a normal physiological rhythm may be modified by training but that training cannot produce such a rhythm. This study was not explicitly designed to isolate variables which may mediate mu enhancement. One such possibility is that subjects increase mu activity by reducing the level of m o to r activity. In the cat, enhanced SMR is observed when animals are trained to inhibit m o t o r responses (Roth et al. 1967). On the other hand, the mu rhythm and SMR are affected primarily by phasic rather than tonic muscle tension and somatosensory stimuli (Chase 1974; Chatrian 1976). Previous studies found no enhancement of mu activity during relaxation training (Bostem et al. 1965) or when subjects were immobilized in water at body temperature (Cohen et al. 1965). Thus the mechanism by which mu activity is enhanced in the quiet alert subject is unclear. Subjects of the present study did not report psychological or subjective effects of the sort reported in the early studies of alpha feedback training (Brown 1970; Nowlis and Kamiya 1970). It was clear that the mu rhythm had no striking psychological correlates and was present as part of the normal waking state. Finally, statistical significance is not synonymous with physiological significance. Whether EEG changes similar to those reported here can in fact account for the reported therapeutic effects of EEG feedback training (Sterman et al. 1974; Finley et al. 1975; Lubar and Bahler 1975, Wyler et al. 1976) remains to be determined by controlled clinical investigations.
293 Summary The mu rhythm is a spontaneous electroencephalographic pattern which can be recorded over human somatosensory cortex in the absence of movement. Power spectral analysis across 50 sessions of EEG feedback training showed that mu activity could be systematically enhanced, whereas the 12--14 c/sec component of low voltage fast activity could not be modified. Results indicate that long-term modification of at least one normal cortical rhythm, initially present in the human EEG, is possible with feedback training but that training cannot produce such a rhythm.
Rdsum~ Apprentissage de feedback E E G : augmentation 1'activit( corticale somatosensitive
de
Le rythme mu est un pattern 61ectroencdphalographique spontand qui peut 6tre enregistrd au niveau du cortex sensorimoteur chez l ' h o m m e en l'absence de mouvement. L'analyse du spectre de puissance faite au cours de 50 s6ances de feedback EEG montre que l'activit6 mu peut 6tre systdmatiquement augmentde, alors que les composantes A 12--14 c/sec de l'activitd rapide de bas voltage ne pouvaient pas 6tre modifi~es. Ces r~sultats indiquent qu'une modification A long terme d'au moins un rythme cortical normal, initialement prdsent dans I'EEG chez l ' a o m m e , est possible au moyen de l'apprentissage du feedback, mais que le seul apprentissage ne peut pas produire un tel rythme.
I thank T. Allison for advice and discussion, and T. Fisher and J. Jasiorkowski for designing and constructing the apparatus.
References Beatty, J., Greenberg, A., Deibler, W.P. and O'HanIon, J.F. Operant control of occipital theta rhythm affects performance in a radar monitoring task. Science, 1974, 183: 871--873. Bostem, F., Dongier, M., Demaret, A. and Herzet, J.P. Discussion on mu rhythms. Electroenceph. clin. Neurophysiol., 1965, 18: 721. Brown, B.B. Recognition of aspects of consciousness through association with EEG alpha activity represented by a light signal. Psychophysiology, 1970, 6: 442--452. Chase, M.H. Research strategies in operant EEG conditioning: an investigation of sensorimotor cortical
294 activity. In: M.H. Chase (Ed.), Operant Control of Brain Activity. Perspectives in the Brain Sciences, Vol. 2. Brain Information Service/Brain Research Institute, UCLA, Los Angeles, Calif., 1974: 39-60. Chase, M.H. and Harper, R.M. Somatomotor and visceromotor correlates of operantly conditioned 12--14 c/sec sensorimotor cortical activity. Eleetroenceph, clin. Neurophysiol., 1971, 31: 85--92. Chatrian, G.E. The mu rhythm. In: A. R~mond (Ed.), Handbook of Electroencephalography and Clinical Neurophysiology, Vol. 6, Part A, The EEG of the Waking Adult. Elsevier, Amsterdam, 1976: 46--69. Cohen, J., Cobb, W. and Dunkley, G. The EEG in conditions of somaesthetic constancy. Electroenceph, clin. Neurophysiol., 1965, 18: 721. Finley, W.W., Smith, H.A. and Etherton, M.D. Reduction of seizures and normalization of the EEG in a severe epileptic following sensorimotor biofeedback training. Biol. Psychol., 1975, 2: 189--203. Hays, W.L. Statistics for Psychologists. Holt, Rinehard and Winston, New York, 1963. Howe, R.C. and Sterman, M.B. Cortical-subcortical EEG correlates of suppressed motor behavior during sleep and waking in the cat. Electroenceph. clin. Neurophysiol., 1972, 32: 681--695. Kaplan, B.J. Biofeedback in epileptics: equivocal relationship of reinforced EEG frequency to seizure reduction. Epilepsia, 1975, 16: 477--485. Kuhlman, W.N. Functional topography of the human mu rhythm. Electroenceph. clin. Neurophysiol., 1978a, 44: 83--93. Kuhlman, W.N. EEG feedback training of epileptic patients: clinical and electroencephalographic analysis. Electroenceph. clin. Neurophysiol., 1978b, 45: in press.
W.N. KUHLMAN Kuhlman, W.N. and Klieger, D.M. Alpha enhancement: effectiveness of two feedback contingencies relative to a resting baseline. Psychophysiology, 1975, 12: 456--460. Lubar, J.F. and Bahler, W.W. Behavioral management of seizures following EEG biofeedback training of the sensorimotor rhythm. Biofeed. Self-Reg., 1976, 1: 77--103. Nowlis, D.P. and Kamiya, J. The control of electroencephalographie alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology, 1970, 6: 476--484. Roth, S.R., Sterman, M.B. and Clemente, C.D. Comparison of EEG correlates of reinforcement, internal inhibition, and sleep. Electroenceph. olin. Neurophysiol., 1967, 23: 509--520. Rougeul, A., Letalle, A. et Corvisier, J. Activit~ rythmique du cortex somesth~sique primaire en relation avec l'immobilit~ chez le chat libre ~veill& Electroenceph. clin. Neurophysiol., 1972, 33 : 23 39. Sterman, M.B., MacDonald, L.R. and Stone, R.K. Biofeedback training of sensorimotor EEG in man and its effects on epilepsy. Epilepsia, 1974, 15: 395--416. Wyler, A.R., Lockard, J.S., Ward, A.A. and Finch, C.A. Conditioned EEG desynchronization and seizure occurrence in patients. Electroenceph. clin. Neurophysiol., 1976, 41 : 501--512. Wyter, A.R., Lockard, J.S., DuCharme, L.L. and Perkins, M.G. EEG operant conditioning in a monkey model. II. EEG spectral analysis. Epilepsia, 1977, 18: 481--488. Wyrwicka, W. and Sterman, M.B. Instrumental conditioning of sensorimotor cortex EEG spindles in the waking cat. Physiol. Behav., ].968, 3: 703-707.
Electroencephalography and Clinical Neurophysiology, 1978, 4 5 : 2 9 0 - - 2 9 4 © Elsevier/North-Holland Scientific Publishers, Ltd.
Laboratory note EEG FEEDBACK TRAINING: ENHANCEMENT OF SOMATOSENSORY CORTICAL ACTIVITY * WILLIAM N. KUHLMAN
Neuropsychology Laboratory, Veterans Administration Hospital, West Haven, Conn. 06516 and Departments of Psychology and Neurology, Yale University, New Haven, Conn. 06520 (U.S.A.) (Accepted for publication: February 14, 1978)
Early studies of EEG feedback training reported that human subjects can alternately enhance and suppress spontaneous rhythmic activity for short periods of time when they are provided with immediate information, or feedback, signalling its occurrence (Brown 1970; Nowlis and Kamiya 1970; Beatty et al. 1974). Although current clinical applications of EEG feedback training assume that training may produce sustained modification in neural activity (Sterman et al. 1974; Kaplan 1975; Wyler et al. 1976), there are relatively few systematic data concerning the effects of unidirectional, long-term feedback training on identified patterns of the human EEG. Operant conditioning of a spontaneous EEG rhythm recorded from somatosensory cortex in the cat, the sensorimotor rhythm or SMR (Wyrwicka and Sterman 1968; Chase and Harper 1971) has often served as an animal model for EEG feedback training in humans (Sterman et al. 1974; Finley et al. 1975; Lubar and Bahler 1976). Recent studies have shown that the human mu rhythm is analogous to the feline SMR in terms of cortical topography, relationship to behavior, and reactivity to sensory stimulation (Chase 1974; Kuhlman 1978a). Before investigating clinical applications of EEG feedback training, I therefore investigated whether long-term enhancement of mu rhythm activity was possible across 50 sessions of EEG feedback training.
Method Five volunteer subjects (4 male, 1 female; 18--36 years), in good health with no history of neurological disease, were comfortably seated in a moderately illuminated recording room and were monitored by * Supported by the Medical Research Serivce of the Veterans Administration, National Institute of Mental Health Grant MH-05286, and by a predoctoral training grant from the Epilepsy Foundation of America.
closed-circuit television. All had previously served in EEG studies and were experienced in maintaining low levels of muscle tension. Bipolar scalp recordings were derived from pairs of disc electrodes placed over somatosensory cortex and occipito-parietal cortex of the left hemisphere (Fig. 1A). Spontaneous EEG activity was continuously recorded on an inkwriter and concurrently on an FM tape recorder for subsequent power spectral analysis on a PDP-12 computer. In an initial session, subjects were tested for the presence of mu activity by the method of Kuhlman (1978a), and control data were collected prior to experimental training. Briefly, 8 channels of EEG were recorded from closely spaced bipolar chains of peri-Rolandic and occipital electrodes. Mu activity was identified as a dominant rhythm which is maximal over somatosensory cortex at rest, is attenuated by voluntary movement or somatosensory stimulation, but is minimally affected by visual stimulation (Fig. 1B; Chatrian 1976 ; Kuhlman 1978a). These spatial and functional characteristics, which are also the characteristics of the feline SMR (Roth et al. 1967; Howe and Sterman 1972; Rougeul et al. 1972; Chase 1974), distinguish mu activity from occipital alpha activity although both rhythms fall within the nominal alpha frequency band (Chatrian 1976; Kuhlman 1978a). Mu rhythm activity, with dominant frequencies of 9--11 c/sec, was identified in 3 subjects, and these subjects were subsequently given feedback for 9--11 c/sec activity. Although previous investigators have assumed that 12--14 c/sec activity in the human EEG is equivalent to the feline SMR (Sterman et al. 1974; Finley et al. 1975; Lubar and Bahler 1976), topographical analysis showed that this activity represented a desynchronized, low voltage fast (LVF) pattern which did not show the spatial or functional characteristics of the SMR or mu rhythm (Fig. 1). For purposes of comparison to feedback training of rhythmic mu activity and to investigate whether LVF activity could be enhanced, the remaining two subjects were given feedback for 12--14 c/sec activity.
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II Fig. 1. A: e x a m p l e s o f d i s t i n c t i v e m u r h y t h m activity (left) a n d d e s y n c h r o n i z e d low voltage, fast activity (right) in q u i e t alert subjects, r e c o r d e d f r o m c e n t r a l a n d o c c i p i t a l scalp e l e c t r o d e s s h o w n at left in r e l a t i o n to t h e 1 0 - - 2 0 s y s t e m o f e l e c t r o d e p l a c e m e n t . B: average p r e t r a i n i n g p o w e r s p e c t r a l d a t a for all s u b j e c t s o f t h e t w o t r a i n i n g groups. Mu activity ( 9 - - 1 1 c/sec) was m a x i m a l at rest, was a t t e n u a t e d d u r i n g self-paced m o v e m e n t o f t h e c o n t r a lateral h a n d b u t was n o t a f f e c t e d b y o p e n i n g or closing t h e eyes. T h e 1 2 - - 1 4 c/sec c o m p o n e n t o f low voltage, fast activity did n o t s h o w t h e s e f u n c t i o n a l characteristics.
E a c h s u b j e c t received 50 2 0 - m i n t r a i n i n g sessions. T w o sessions were c o n d u c t e d p e r day, 2--3 days p e r w e e k , over a p e r i o d o f 2--3 m o n t h s . E E G activity f r o m t h e c e n t r a l e l e c t r o d e s was first passed t h r o u g h a n a l o g filters s e t t o a t t e n u a t e activity o u t s i d e t h e freq u e n c y range for f e e d b a c k . Pass b a n d s w e r e : 7 - - 1 3 Hz for t h e 9 - - 1 1 c/sec t r a i n i n g group, a n d 1 0 - - 1 6 Hz for t h e 1 2 - - 1 4 c/sec t r a i n i n g g r o u p ; a t t e n u a t i o n was 24 d B / o c t a v e . O u t p u t was essentially flat w i t h i n t h e f r e q u e n c y range o f f e e d b a c k . F i l t e r e d E E G was t h e n fed t o a digital d i s c r i m i n a t o r circuit w h i c h m e a s u r e d t h e p e r i o d a n d a m p l i t u d e o f successive cycles o f activity a n d a u t o m a t i c a l l y c o n t r o l l e d f e e d b a c k p r e s e n t a t i o n 1. F o r t h e m u r h y t h m g r o u p , criteria weep 5 suc1 P e r i o d d e t e c t i o n was b y zero-crossing analysis. A m o r e d e t a i l e d d e s c r i p t i o n o f this circuit is available from the author.
cessive cycles w i t h i n t h e 9 - - 1 1 c/sec range h a v i n g a m i n i m u m a m p l i t u d e o f 10/~V. Less s t r i n g e n t criteria for L V F activity were necessary due to t h e low amplit u d e a n d s h o r t d u r a t i o n c h a r a c t e r i s t i c of this activity. Criteria were t w o successive cycles w i t h i n t h e 1 2 - - 1 4 c/sec range, w i t h an a m p l i t u d e greater t h a n 2 ~tV. W h e n criteria were m e t , a small w h i t e light in t h e r e c o r d i n g r o o m was i l l u m i n a t e d a n d r e m a i n e d o n as l o n g as specified activity was p r e s e n t . B e l o w t h e feedb a c k light, a digital c o u n t e r a c c u m u l a t e d t h e n u m b e r o f s e c o n d s t h e light was o n d u r i n g t h e t r a i n i n g session. S u b j e c t s were i n s t r u c t e d t o a t t e m p t t o k e e p t h e l i g h t o n as m u c h as possible a n d t o a d v a n c e t h e c o u n t e r t o a n u m b e r h i g h e r t h a n d u r i n g p r e c e d i n g sessions. E x c e p t as n o t e d later, n o t a n g i b l e r e w a r d was c o n t i n g e n t u p o n p e r f o r m a n c e , a l t h o u g h all s u b j e c t s were paid for p a r t i c i p a t i n g . P o w e r spectral analysis o f E E G a c t i v i t y r e c o r d e d
292
W.N. K U H L M A N
during each session, i n d e p e n d e n t of selective filtering by the feedback apparatus, provided quantitative measures of E E G change. Before input to the computer, E E G signals were filtered at 3 Hz (high pass) and 40 Hz (low pass) to eliminate artifactual signals. Power spectra were calculated for individual 4-sec epochs of activity digitized at a sample rate o f 256 points per epoch. An average of 128 4-sec spectra, sequentially sampled from the middle of a session, provided a representative p o w e r s p e c t r u m for each session.
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F e e d b a c k training led to reliable increases in mu r h y t h m (Fig. 2). Since current clinical applications of E E G feedback imply a cumulative effect of long-term training ( S t e r m a n et al. 1974; Finley et al. 1975; Kaplan 1975), linear regression analyses (Hays 1963} with a d o p t e d c o n f i d e n c e levels of P < 0.01 ( d f = 1, 48) were c o n d u c t e d on data f r o m individual subjects to d e t e r m i n e if a significant p r o p o r t i o n of the total variance could be a c c o u n t e d for by a linear f u n c t i o n of training experience. The regression analyses s h o w e d significant e n h a n c e m e n t of 9--11 c/sec activity in each subject of the m u r h y t h m group across the 50 sessions (F > 10.4). The average increase in m u activity w a s 143% as derived f r o m the regression equations. Activity e x h i b i t e d in the first 5 training sessions was n o t significantly different f r o m eyesopen and eyes-closed resting values o b t a i n e d prior to training. The increase in m u activity was n o t secondary to a non-specific increase in all activity across the f r e q u e n c y s p e c t r u m since the p r o p o r t i o n o f p o w e r in the 9--11 c/sec range relative to total p o w e r from 3 to 30 c/sec also significantly increased ( F > 8.28). Central 12--14 clsec activity did n o t reliably change, nor did 9--11 c/sec activity r e c o r d e d f r o m the occipital electrodes significantly vary as a f u n c t i o n of training. Analysis of the peak f r e q u e n c y of mu activity across sessions, i n d e p e n d e n t of variations in power, s h o w e d that the increase in 9--11 c/sec activity did not arise f r o m a shift in mu frequency. In contrast to these results, no e n h a n c e m e n t was seen in the E E G spectra o f subjects given feedback training for 12--14 c/sec L V F activity (Fig. 2). Absolute and logarithmic transforms o f p o w e r in the 12--14 c/sec range did n o t significantly increase. This activity r e m a i n e d desynchronized, low in voltage, and a r h y t h m did n o t develop with training. Since p o w e r spectral analysis is m a x i m a l l y sensitive to periodic signals, a wave-count analysis was c o n d u c t e d by playing tape-recorded EEG signals through the feedback d e t e c t i o n apparatus set at m i n i m u m threshold (1 cycle onset, 1 cycle offset, 1 p V a m p l i t u d e threshold). This analysis also showed no increase in 12--14
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Fig. 2. Upper: examples of p o w e r spectra of EEG activity recorded f r o m the central electrodes in individual subjects given feedback training to enhance 9--11 c/sec mu r h y t h m activity (left) or 12--14 c/sec low voltage fast ( L V F ) activity right. The spectra (0.25 c/sec resolution) are p l o t t e d across 50 20-min training sessions. A c t i v i t y below 3 c/sec was filtered out; p o w e r in arbitrary units. The apparent decrease in low f r e q u e n c y activity in the L V F subject's spectra (scaled higher for visualization of 12--14 c/sec activity) was n o t seen in o t h e r subjects. Lower: average p o w e r spectral data during training for all subjects of the training groups. Three f r e q u e n c y bands of interest are plotted as a f u n c t i o n of 10 blocks of 5 sessions each. All subjects given feedback training for 9--11 c/sec mu activity (left) showed increases in this activity. The increase on overall p o w e r (3--30 c/sec) is due to increased m u activity; 12--14 c/sec activity did n o t significantly change. In subjects given feedback training for 12--14 c/sec low voltage fast activity (right) no e n h a n c e m e n t was seen in any frequency band.
c/sec activity across sessions. To investigate the possibility that lack of m o t i v a t i o n c o n t r i b u t e d to the failure of subjects to increase 12--14 c/sec activity, one subject was given an additional 10 sessions of training in which she was paid a m o n e t a r y reward for each second of 12--14 c/sec activity. However,
FEEDBACK T R A I N I N G OF MU RHYTHM 12--14 c/sec activity did not increase and was not significantly different from the previous 50 sessions. In the power spectra of subjects given feedback for LVF activity, 9--11 c/sec activity did not significantly change, indicating that EEG activity in this frequency range does not automatically increase as a function of time in the experimental situation. In a subsequent study (Kuhlman 1978b), mu rhythm activity did not spontaneously increase when subjects were given feedback for other EEG patterns.
Discussion The results of this study show that enhancement ~f at least one clearly defined human EEG rhythm is possible across long-term feedback training and lend further support to the analogy between the human mu rhythm and the feline SMR. However, this does not imply that all forms of human cortical activity may be similarly modified. The 12--14 c/sec c o m p o n e n t of L V F activity could not be enhanced with training, consistent with previous reports of difficulty in effecting selective increases in activity not initially present as an identifiable entity in the EEG (Kaplan 1975; Kuhlman and Klieger 1975; Wyler et al. 1977). Thus, it would appear that the occurrence of a normal physiological rhythm may be modified by training but that training cannot produce such a rhythm. This study was not explicitly designed to isolate variables which may mediate mu enhancement. One such possibility is that subjects increase mu activity by reducing the level of m o to r activity. In the cat, enhanced SMR is observed when animals are trained to inhibit m o t o r responses (Roth et al. 1967). On the other hand, the mu rhythm and SMR are affected primarily by phasic rather than tonic muscle tension and somatosensory stimuli (Chase 1974; Chatrian 1976). Previous studies found no enhancement of mu activity during relaxation training (Bostem et al. 1965) or when subjects were immobilized in water at body temperature (Cohen et al. 1965). Thus the mechanism by which mu activity is enhanced in the quiet alert subject is unclear. Subjects of the present study did not report psychological or subjective effects of the sort reported in the early studies of alpha feedback training (Brown 1970; Nowlis and Kamiya 1970). It was clear that the mu rhythm had no striking psychological correlates and was present as part of the normal waking state. Finally, statistical significance is not synonymous with physiological significance. Whether EEG changes similar to those reported here can in fact account for the reported therapeutic effects of EEG feedback training (Sterman et al. 1974; Finley et al. 1975; Lubar and Bahler 1975, Wyler et al. 1976) remains to be determined by controlled clinical investigations.
293 Summary The mu rhythm is a spontaneous electroencephalographic pattern which can be recorded over human somatosensory cortex in the absence of movement. Power spectral analysis across 50 sessions of EEG feedback training showed that mu activity could be systematically enhanced, whereas the 12--14 c/sec component of low voltage fast activity could not be modified. Results indicate that long-term modification of at least one normal cortical rhythm, initially present in the human EEG, is possible with feedback training but that training cannot produce such a rhythm.
Rdsum~ Apprentissage de feedback E E G : augmentation 1'activit( corticale somatosensitive
de
Le rythme mu est un pattern 61ectroencdphalographique spontand qui peut 6tre enregistrd au niveau du cortex sensorimoteur chez l ' h o m m e en l'absence de mouvement. L'analyse du spectre de puissance faite au cours de 50 s6ances de feedback EEG montre que l'activit6 mu peut 6tre systdmatiquement augmentde, alors que les composantes A 12--14 c/sec de l'activitd rapide de bas voltage ne pouvaient pas 6tre modifi~es. Ces r~sultats indiquent qu'une modification A long terme d'au moins un rythme cortical normal, initialement prdsent dans I'EEG chez l ' a o m m e , est possible au moyen de l'apprentissage du feedback, mais que le seul apprentissage ne peut pas produire un tel rythme.
I thank T. Allison for advice and discussion, and T. Fisher and J. Jasiorkowski for designing and constructing the apparatus.
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