EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG

TL;DR

This paper introduces EEG-EMG FAConformer, a novel multimodal deep learning model that fuses EEG and EMG signals using frequency-aware attention modules to improve motor pattern recognition in brain-computer interfaces.

Contribution

The paper presents a new frequency-aware Conv-Transformer architecture with specialized modules for EEG and EMG fusion, outperforming existing methods in motor pattern recognition tasks.

Findings

Outperforms existing methods on Jeong2020 dataset

Demonstrates high robustness and stability

Effectively encodes frequency and temporal information

Abstract

Motor pattern recognition paradigms are the main forms of Brain-Computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What's more, modules like Multi-Scale Fusion Module, Independent Channel-Specific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEG-EMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability.