Generating a fuzzy rule-based brain-state-drift detector by riemann-metric-based clustering

Publication Type:
Conference Proceeding
Citation:
2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017, 2017-January pp. 1220 - 1225
Issue Date:
2017-11-27
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IEEE SMC 2017 final.pdfAccepted Manuscript1.11 MB
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© 2017 IEEE. Brain-state drifts could significantly impact on the performance of machine-learning algorithms in brain computer interface (BCI). However, less is understood with regard to how brain transition states influence a model and how it can be represented for a system. Herein we are interested in the hidden information of brain state-drift occurring in both simulated and real-world human-system interaction. This research introduced the Riemann metric to categorize EEG data, and visualized the clustering result so that the distribution of the data can be observable. Moreover, to defeat subjective uncertainty of electroencephalography (EEG) signals, fuzzy theory was employed. In this study, we built a fuzzy rule-based brain-statedrift detector to observe the brain state and imported data from different subjects to testify the performance. The result of the detection is acceptable and shown in this paper. In the future, we expect that brain-state drifting can be connected with human behaviors via the proposed fuzzy rule-based classification. We also will develop a new structure for a fuzzy rule-based brain-statedrift detector to improve the detection accuracy.
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