Applying a Hybrid Genetic Algorithm in the Design of a Self-Paced Brain Interface with a Low False Positive Rate

A new hybrid genetic algorithm (HGA) for optimization of a self-paced brain interface (SBI) is proposed. To identify intentional control (IC) commands in the noisy background EEG signal, the proposed SBI uses features extracted from three neurological phenomena - movement-related potentials as well...

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Bibliographische Detailangaben

Hauptverfasser:	Fatourechi, M., Birch, G. E., Ward, R. K.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithm design and analysis Background noise Biomedical signal processing Data analysis Electroencephalography Feature extraction Genetic algorithms Integrated circuit noise Pattern classification Potential well Rhythm Signal processing Wavelet transforms
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