Choi, Young-Seok
(Department of Electronic Engineering, Gangneung-Wonju National University)
,
Shin, Hyun-Chool
(School of Electronic Engineering, Soongsil University)
,
Ying, Sarah H.
(Johns Hopkins University School of Medicine, Dept. Radiology)
,
Newman, Geoffrey I.
(Johns Hopkins University School of Medicine, Dept. Biomedical Eng.)
,
Thakor, Nitish
(Johns Hopkins University School of Medicine, Dept. Biomedical Eng.)
소뇌 운동실조는 점차 진행되는 신경퇴행질병이며 운동 조절을 위한 기능의 상실을 동반하기에 환자의 삶을 심각하게 저하시킨다. 소뇌 운동실조 환자는 운동제어 과정에서 부적절한 폐회로 소뇌 반응으로 인해 운동 명령이 제한된다. 본 논문에서는 최근 뇌-컴퓨터 인터페이스 기술을 이용하여 소뇌의 이상으로 인한 운동실조 환자들이 외부기기를 제어할 수 있도록 운동상상 기반의 뇌파의 특성을 분석하고 이를 이용한 뇌-컴퓨터 인터페이스 기법을 제안한다. 뇌파 기반의 뇌-컴퓨터 인터페이스의 효용성을 검증하기 위하여 소뇌 운동실조 환자와 정상인 그룹에서 운동상상에 따른 뮤밴드 파워를 조절하는 능력을 비교하였다. 이를 통하여 소뇌 운동실조 환자에의 뇌-컴퓨터 인터페이스의 가능성을 보여준다.
소뇌 운동실조는 점차 진행되는 신경퇴행질병이며 운동 조절을 위한 기능의 상실을 동반하기에 환자의 삶을 심각하게 저하시킨다. 소뇌 운동실조 환자는 운동제어 과정에서 부적절한 폐회로 소뇌 반응으로 인해 운동 명령이 제한된다. 본 논문에서는 최근 뇌-컴퓨터 인터페이스 기술을 이용하여 소뇌의 이상으로 인한 운동실조 환자들이 외부기기를 제어할 수 있도록 운동상상 기반의 뇌파의 특성을 분석하고 이를 이용한 뇌-컴퓨터 인터페이스 기법을 제안한다. 뇌파 기반의 뇌-컴퓨터 인터페이스의 효용성을 검증하기 위하여 소뇌 운동실조 환자와 정상인 그룹에서 운동상상에 따른 뮤밴드 파워를 조절하는 능력을 비교하였다. 이를 통하여 소뇌 운동실조 환자에의 뇌-컴퓨터 인터페이스의 가능성을 보여준다.
Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropri...
Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.
Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.
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가설 설정
Although the literature states that the EEG pattern is “normal” in cerebellar degeneration, this presumably refers to the lack of heightened epileptogenic potential, which does not necessarily indicate that the EEG is comparable to that of unaffected individuals. We hypothesize that cortical regions of the corticocerebellar circuit may show functional abnormalities when they are connected to areas of primary cerebellar degeneration. The strong interconnections between the cerebellum and the cerebral cortex most likely contribute to the distortion in the processing of sensory feedback.
제안 방법
During each trial, subjects were visually cued either to enter a state of relaxation (target appears at the top of a computer screen) or to imagine motor movement (target appears at the bottom). A three-state (move up, move down, remain still) EEG-based BCI was used to control the position of a cursor in one dimension on a computer screen.
In order to demonstrate feasibility in this first study of non-invasive, EEG-based BCI in cerebellar ataxia, we assessed the ability to modulate mu-band power during a cued motor imagery task. We also examined possible differences in performance associated with ataxia, as these differences might necessitate modification in BCI decoding algorithms.
Our primary endpoint was to evaluate the possibility that ataxia patients could achieve control of a BCI using cued motor imagery. Indeed, subjects were able to achieve mean trial success of greater than 13.
To achieve a trial success case, the sum of F(tk) over all tk within that trial must equal +7 in the relaxation trials, and -7 in the motor imagery trials before 15 seconds elapse. Subjects underwent 16 trials each set (8 relaxation and 8 movement imagery trials), with a pseudo-randomized order of presentation within each set.
이론/모형
In addition, Burg's spectral estimation method was used to estimate the time-varying AR coefficients.
참고문헌 (14)
C. Guger, G. Edlinger, W. Harkam, I. Niedermayer, G. Pfurtscheller, "How many people are able to operate an EEG-based brain-computer interface (BCI)?", IEEE Trans Neural Systems and Rehabilitation Engineering, vol. 11, no. 2 pp. 145-147, June 2003.
D. J. McFarland, L. M. McCane, S. V. David, J. R. Wolpaw, "Spatial filter selection for EEG-based communication", Electroencephalography Clinical Neurophysiology, vol. 103, pp. 386-394, 1997.
L. A. Liversedge, V. Emery, "Electroencephalographic changes in cerebellar degenerative lesions", Journal of Neurology and Neurosurgery Psychiatry, vol. 24, no. 4, pp. 326-330, 1961.
L. Schols, C. Linnemann, C. Globas, "Electrophysiology in spinocerebellar ataxias: Spread of disease and characteristic findings", The cerebellum, pp. 198-203, 2008.
M. Arai, H. Tanaka, R. D. Pascual-Marqui, K. Hirata, "Reduced brain electric activities of frontal lobe in cortical cerebellar atrophy", Clinical Neurophysiology, vol. 114, no. 4, pp.740-747, 2003.
J. S. Kwon, B. F. O'Donnell, G. V. Wallenstein, R. W. Greene, Y. Hirayasu, P. G. Nestor, M. E. Hasselmo, G. F. Potts, M. E. Shenton, R. W. McCarley, "Gamma frequency-range abnormalities to auditory stimulation in schizophrenia", Archives of General Psychiatry, vol. 56, no. 11, pp. 1001-1005, Nov. 1999.
B. A. Clementz, M. A. Geyer, D. L., Braff, "P50 Suppression among schizophrenia and normal comparison subjects: A methodological analysis", Biological Psychiatry, vol. 41, no. 10, pp. 1035-1044, May 1997.
F. Battaglia, A. Quartarone, M. F. Ghilardia, R. Dattola,S. Bagnato, V. Rizzo, L. Morgante, P. Girlanda, "Unilateral cerebellar stroke disrupts movement preparation and motor imagery", Clinical Neurophysiology, vol. 117, no. 5, pp. 1009-1016, 2006.
B. Gonzalez, M. Rodriquez, C. Ramirez, M. Sabate, "Disturbance of Motor Imagery After Cerebellar Stroke". Behavioral Neuroscience, vol. 119, no. 2, pp. 622-626 Apr. 2005.
S. H. Ying, S. I. Choi, S. L. Perlman, R. W. Baloh, D. S. Zee, A. W. Toga, "Pontine and cerebellar atrophy correlate with clinical disability in SCA2", Neurology, vol. 66, no. 3, pp. 424-42, Feb. 2006.
A. Chatterjee, V. Aggarwal, A. Ramos, S. Acharya, N. V. Thakor, "A brain-computer interface with vibrotactile biofeedback for haptic information." Journal of NeuroEngineering and Rehabilitation, vol. 4, no. 1, pp. 40, Oct. 2007.
R. Bos, S. deWaele, P. Broersen, "Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data", IEEE Transactions on Instrumentation and Measurement. vol. 51, no. 6, pp. 1289-1294, Dec. 2002.
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