A Generic Image-Based Features Classification System for Brain-Computer Interface System [Extended Abstract]
نویسندگان
چکیده
This paper presents a generic image-based features classification system for EEG-based Brain-Computer Interface (BCI) system which may be applied to any kind of mental tasks. The time-frequency analysis (TFA) and image processing technique are used to extract image-based features from the EEG signal. The feature templates, called Matched Filter, are performed to form feature vector with which the set of mental task can be discriminated by linear discriminant analysis (LDA). In addition, the discriminative capabilities of the set of mental task and the optimum matched filter are further enhanced using the cooperative coevolutionary genetic algorithm (CoCGA).
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