EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG
ZhengXiao He, Minghong Cai, Letian Li, Siyuan Tian, Ren-Jie Dai

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.…
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Taxonomy
TopicsMuscle activation and electromyography studies · EEG and Brain-Computer Interfaces · Neuroscience and Neural Engineering
MethodsSoftmax · Attention Is All You Need · Convolution
