Brain–computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition

Abstract

Brain–computer interface is a new form of interaction between humans and machines. This interaction helps the human brain control or operate external devices directly using electroencephalograph (EEG) signals. In this study, we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies. Then, we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials. Afterward, we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate. Finally, we build a virtual household electrical appliance brain–computer control interface, which achieves over 72.84% accuracy for three classification problems. Keywords: Brain–computer interface, Steady-state visually evoked potential, Canonical correlation analysis