Motor imagery EEG classification algorithm based on improved lightweight feature fusion network

When deep learning techniques are introduced for Motor Imagery(MI) EEG signal classification, a multitude of state-of-the-art models, cannot be trained effectively because of the relatively small datasets. Proposing a model specialized for MI EEG signals classification plays a prominent role in promoting the combination of deep learning technology and MI EEG signal classification. In this paper, a novel Lightweight Feature Fusion Network (LFANN) based on an improved attention mechanism and tensor decomposition approach has been introduced. The proposed algorithm has been evaluated on a public benchmark dataset from BCI Competition IV, and the original dataset has been augmented with Enhance-Super-Resolution Generative Adversarial Network(ESRGAN). The experimental results demonstrate that the average accuracy of 91.58% and the average Kappa value of 0.881 can be achieved through the proposed algorithm. Furthermore, the compressed LAFFN, whose parameters have been compressed nearly ten times, creates no significant difference in performance compared to LAFFN. The investigation carried out through this experiment has provided novel insights into the classification research for MI EEG signals.

Automated summary

A novel Lightweight Feature Fusion Network (LFANN) based on an improved attention mechanism and tensor decomposition approach is proposed for Motor Imagery (MI) EEG signal classification. Experimental results demonstrate high accuracy and Kappa value achieved by the proposed algorithm.