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％.
关键词
共空间模式/运动想象/事件相关频谱扰动/融合特征信息/支持向量机
Key words
common space model/imagination of motion/event-related spectrum disturbances/integration of characteristic infor-mation/support vector machines
维普
万方数据