Recognition of Electroencephalographic Signals in Motor Imaging Based on Feature Fusion and Particle Swarm Optimization

The signal-to-noise ratio of EEG signal is low, feature extraction and feature selection are difficult, and high classification accuracy cannot be obtained. To solve these problems, this paper extracts the features of time domain, frequency domain and space domain, and uses particle swarm optimization algorithm combined with random forest classifier to screen the features. The specific process is as follows: firstly, the signal is bandpass filtered according to the R² graph; then, the wavelet soft thresholding and scoring common space pattern algorithm are used for denoising and channel filtering; furthermore, the time-frequency domain and space domain features are extracted through three algorithms, and the evaluation index based on the random forest classifier is used as the fitness function of particle swarm optimization (PSO) after feature fusion for feature selection; finally, three classifiers and integrated classifiers are used to verify the effect. The experimental results show that through feature fusion and feature selection, redundant information can be removed and effective information can be retained. The final classification accuracy is 98.3%, which provides a new method for the application of this technology in medical rehabilitation and other fields.