Hui Wang1, Aiguo Song1, Bowei Li1, Baoguo Xu1, Yangming Li2. 1. School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China. 2. Biorobotics Lab, Electrical Engineering Department, University of Washington, Seattle, WA, USA.
Abstract
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).
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).
Entities:
Keywords:
EEG-based brain-computer interface (BCI); Hilbert-Huang transform; feature extraction; mental task of relaxation-meditation; mental task of tension-imagination; pattern classification