# MS-TSEFNet: Multi-Scale Spatiotemporal Efficient Feature Fusion Network

## TL;DR

This paper introduces MS-TSEFNet, a new deep learning model that improves the accuracy of decoding motor imagery from EEG signals.

## Contribution

The novel contribution is a multi-scale spatiotemporal feature fusion network that enhances EEG signal classification performance.

## Key findings

- MS-TSEFNet achieves average classification accuracies of 80.31%, 86.69%, and 71.14% on three public datasets.
- The model outperforms state-of-the-art algorithms in motor imagery signal decoding.
- Ablation studies confirm the effectiveness of the multi-scale convolution and feature fusion modules.

## Abstract

Motor imagery signal decoding is an important research direction in the field of brain–computer interfaces, which aim to judge the motor imagery state of an individual by analyzing electroencephalogram (EEG) signals. Deep learning technology has been gradually applied to EEG classification, which can automatically extract features. However, when processing complex EEG signals, the existing decoding models cannot effectively fuse features at different levels, resulting in limited classification performance. This study proposes a multi-scale spatiotemporal efficient feature fusion network (MS-TSEFNet), which learns the dynamic changes in EEG signals at different time scales through multi-scale convolution modules and combines the spatial attention mechanism to efficiently capture the spatial correlation between electrodes in EEG signals. In addition, the network adopts an efficient feature fusion strategy to deeply fuse features at different levels, thereby improving the expression ability of the model. In the task of motor imagery signal decoding, MS-TSEFNet shows higher accuracy and robustness. We use the public BCIC-IV2a, BCIC-IV2b and ECUST datasets for evaluation. The experimental results show that the average classification accuracy of MS-TSEFNet reaches 80.31%, 86.69% and 71.14%, respectively, which is better than the current state-of-the-art algorithms. We conducted an ablation experiment to further verify the effectiveness of the model. The experimental results showed that each module played an important role in improving the final performance. In particular, the combination of the multi-scale convolution module and the feature fusion module significantly improved the model’s ability to extract the spatiotemporal features of EEG signals.