Psychophysiological classification and experiment study for spontaneous EEG based on two novel mental tasks

• Published in: Technology and health care Vol. 23; no. 2_suppl; pp. S249 - S262
• Main Authors: Wang, Hui, Song, Aiguo, Li, Bowei, Xu, Baoguo, Li, Yangming
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2015
• Subjects: Adult; Algorithms; Brain-Computer Interfaces; Consciousness - physiology; Electroencephalography; Female; Humans; Imagination - physiology; Male; Meditation; Occipital Lobe - metabolism; Parietal Lobe - metabolism; Relaxation - physiology; Signal Processing, Computer-Assisted; mental task of relaxation-meditation; pattern classification; mental task of tension-imagination; feature extraction; EEG-based brain-computer interface (BCI); Hilbert-Huang transform
• ISSN: 0928-7329; 1878-7401; 1878-7401
• DOI: 10.3233/THC-150960

BACKGROUND: Study of imagination offers a perfect setting for study of a large variety of states of consciousness. OBJECTIVE: Here, we studied the characteristics of two electroencephalographic (EEG) patterns evoked by two different imaginary tasks and evaluated the binary classification performance. METHODS: Fifteen individuals (11 male and 4 female, age range of 22 to 33) participated in five sessions of 32-channel EEG recordings. Only by analyzing the subjects' output EEG signals from the central parieto-occipital region of PZ electrode, under the circumstances of consciousness of relaxation-meditation or tension-imagination, we carried out the experiment of feature extraction for spontaneous EEG, as the subjects were blindfolded but asked to open their eyes all the same. The Hilbert-Huang Transform (HHT) was utilized to obtain the Hilbert time-frequency amplitude spectrum, and then with the feature vector set extracted, a two-class Fisher linear discriminant analysis classifier was trained for classification of data epochs of those two tasks. RESULTS: The overall result was that about 90% (± 5%) of the epochs could be correctly classified to their originating task. CONCLUSION: This study not only brings new opportunities for consciousness studies, but also provides a new classification paradigm for achieving control of robots based on the brain-computer interface (BCI).