지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.
Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) was attained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventional method (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.
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참고문헌 (26)
참고문헌 신청
A. M. Owen / 2007 / Using functional magnetic resonance imaging to detect covert awareness in the vegetative state / Arch. Neurol 64 : 1098 ~ 1102
A. M. Owen / 2008 / Detecting awareness in the vegetative state / Molecular and Biophysical Mechanisms of Arousal, Alertness, and Attention 1129 : 130 ~ 138
A. S. Gevins / 1979 / EEG patterns during 'cognitive' tasks. I. Methodology and analysis of complex behaviors / Electroencephalogr. Clin. Neurophysiol 47 : 693 ~ 703
A. S. Gevins / 1979 / EEG patterns during ‘cognitive’ tasks. II. Analysis of controlled tasks / Electroencephalogr. Clin. Neurophysiol 47 : 704 ~ 710
A. S. Royer / 2009 / Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms / J. Neural Eng 6
A. M. Owen / 2008 / Detecting awareness in the vegetative state / Molecular and Biophysical Mechanisms of Arousal, Alertness, and Attention 1129 : 130 ~ 138
A. S. Gevins / 1979 / EEG patterns during 'cognitive' tasks. I. Methodology and analysis of complex behaviors / Electroencephalogr. Clin. Neurophysiol 47 : 693 ~ 703
A. S. Gevins / 1979 / EEG patterns during ‘cognitive’ tasks. II. Analysis of controlled tasks / Electroencephalogr. Clin. Neurophysiol 47 : 704 ~ 710
A. S. Royer / 2009 / Goal selection versus process control in a brain-computer interface based on sensorimotor rhythms / J. Neural Eng 6
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