Channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition

Abstract

In order to solve the problems of weak generalization ability and low classification accuracy in motor imagery EEG signal classification, this paper proposes a channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition. First, the short-time Fourier transform is used to obtain the EEG time-frequency map. Then, it builds a new feature pyramid network(FPN). The attention mechanism module is integrated into the FPN module, and the channel spatial weighted fusion-oriented feature pyramid network is proposed. This new structure can not only learn the weight of important channel features in the feature map, but also learn the representation of important feature areas in the network layers. Meanwhile, Skip-FPN module is added into the network structure, which fuses more details of EEG signals through short connections. The Dropout layer is added to prevent network training from over-fitting. In the classification model, we improve the AdaBoost algorithm to automatically update the base learner according to the classification error rate. Finally, the proposed model is used to classify the test data and the Kappa value is used as the evaluation index. Compared with the state-of-the-art motor image EEG signal recognition methods, the proposed method achieves better performance on the BCI Competition IV 2b data set. It has good generalization ability and can improve the classification effect.

Enhanced grasshopper optimization algorithm with extreme learning machines for motor‐imagery classification

“…In recent decades, the motor imagery is extensively utilized in the BCI systems for the disabled peoples to control the external appliances [1,2]. The other techniques used to assess the motor imagery tasks in the BCI systems are functional magnetic resonance imaging [3,4], electrocorticography [5], magneto-encephalography, positron emission tomography [6], EEG, etc.…”