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

• Published in: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 4; pp. IV-1157 - IV-1160
• Main Authors: Fatourechi, M., Birch, G. E., Ward, R. K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2007
• Subjects: 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

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...