Evidence Accumulation Approach applied to EEG Analysis
; Carreiras, C. C.
Fred, A. L. N.
Evidence Accumulation Approach applied to EEG Analysis, Proc International Conf. on Pattern Recognition Applications and Methods - ICPRAM, Barcelona, Spain, Vol. 0, pp. 479 - 484, February, 2013.
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Human-machine interaction is a rapidly expanding field which benefits from automatic emotion recognition. Therefore, methods that can automatically detect the emotional state of a person are important for this field, as well as for fields such as psychology and psychiatry. This paper proposes the use of clustering ensembles (CEs) to achieve such detection. We use CEs on a dataset containing EEG signals from subjects who performed a stress-inducing task. From the raw EEG data we apply filtering and processing techniques leading to three dataset types: simple EEG, EEG with eye-movement artifacts removed through Independent Component Analysis, and data-driven modes extracted using Empirical Mode Decomposition. Then, for each of these three data types, we compute band power features and phase-locking factors, yielding a total of six different feature spaces. These spaces are then analyzed using the CE framework which combines results of multiple clustering algorithms in a voting scheme. This procedure yields interesting clusters, in particular a natural tendency for finding low numbers of clusters per subject and finding clusters which are composed of consecutive test lines. These two facts combined may indicate that a change in the emotional state of the subject was detected by the proposed framework.