基于 GAF 和混合模型的运动想象分类研究

As a paradigm of brain-computer interface, motor imagery has a broad application prospect in the field of medical rehabilitation. Due to the non-stationarity and low signal-to-noise ratio of Electroencephalograph (EEG) signals, how to effectively extract the features of motor imagery signals and achieve accurate recognition is a key issue in the motor imagery brain-computer interface technology. Aiming at the classification and recognition problem of motor imagery brain-computer interface, this paper proposes a new method combining Gramian Angular Field (GAF) theory, Convolutional Neural Networks, and Long Short-Term Memory (LSTM). First of all, The Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF) in GAF are used respectively. GADF algorithm represents one-dimensional motor imagery EEG signals into two-dimensional images. Then, a targeted shallow Convolutional Neural Network (CNN) model is designed to realize the recognition of the image features to complete the motor imagery classification. A 4-class validation on the BCI Competition IV 2a public dataset is performed on the motor imagery task. The experimental results indicate that, in both single-subject and multi-subject scenarios, the GASF-CNN-LSTM and GADF-CNN-LSTM models exhibit significant performance improvements compared to other state-of-the-art models. Their accuracies surpass 87.66%, with the highest accuracy reaching 99.09%. Moreover, these models demonstrate strong performance when handling data from patients with motor functional disorders, further confirming the effectiveness of the models. In this paper, the time dependence and the image generation and representation technology of the corresponding features of the motor image EEG are discussed, which provides a new idea for the feature mining of the motion image EEG.