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

Issue title: Papers from the 3rd International Conference on Biomedical Engineering and Technology (iCBEB 2014), 25–28 September 2014, Beijing, China

Subtitle:

Guest editors: Edward J. Ciaccio

Article type: Research Article

Authors: Wang, Huia; * | Song, Aiguoa | Li, Boweia | Xu, Baoguoa | Li, Yangmingb

Affiliations: [a] School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China | [b] Biorobotics Lab, Electrical Engineering Department, University of Washington, Seattle, WA, USA

Correspondence: [*] Corresponding author: Hui Wang, School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China. Tel.: +86 15055136607; Fax: +86 0551 65592420; E-mail:wanghui@iim.ac.cn

Keywords: EEG-based brain-computer interface (BCI), mental task of relaxation-meditation, mental task of tension-imagination, Hilbert-Huang transform, feature extraction, pattern classification

DOI: 10.3233/THC-150960

Journal: Technology and Health Care, vol. 23, no. s2, pp. S249-S262, 2015

Published: 2015
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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).

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