A frequency-temporalspatial method for motor-related electroencephalography pattern recognition by comprehensive feature optimization
Either imagined or actual movements lead to a combination of electroencephalography signals with distinctive frequency, temporal and spatial characteristics, which correspond to various motor-related neural activities. This frequency-temporalspatial pattern is the key of motor intention decoding which is the basis of braincomputer interfaces by motor imagery. We present a new method for motor-related electroencephalography recognition which comprehensively optimizes the frequencytimespace features in a user-specific way. The recognition work focuses on three points: proper time and frequency domain segmentation, spatial optimization based on common spatial pattern filters and feature importance evaluation. We show that by combining the advantages of these optimizational methods, the proposed algorithm effectively improves motor task classification, and the recognized signal chanracteristics can be used to visualize the motor related electroencephalography patterns under different conditions.
feature optimization braincomputer interface motor tasks csp