Multi-domain Fusion Feature EEG Signals Classification Based on Event-related Spectral Perturbation
Aiming at the problem that the common spatial pattern algorithm cannot obtain sufficient spatial distribution information when processing EEG signals with low channel counts and multi-modal imaginative actions,resulting in low classification accuracy,proposing a feature extraction method in time-frequency and spatial domains based on event-related spectral perturbations.Firstly,ac-cording to the characteristics of the independent functional mapping area of the limb imagination in the motor sensory cortex region,the event-related time-frequency feature information with significant differences under specific leads was extracted and fused with the spatial feature information of specific leads.Finally,the parameter-optimized support vector machine was used to identify different classes of limb-imagined actions.Comparison of the experimental results shows that the recognition performance of fused features in multi-modal imagined actions is significantly improved over single features.It can not only obtain more comprehensive EEG feature information,but also effectively reduce the demand for multi-channel number,and its average classification accuracy reaches 93.1％.
common space modelimagination of motionevent-related spectrum disturbancesintegration of characteristic infor-mationsupport vector machines
万方数据
维普
