IEEE Transactions on Neural Systems and Rehabilitation Engineering (Jan 2024)

FACT-Net: A Frequency Adapter CNN With Temporal-Periodicity Inception for Fast and Accurate MI-EEG Decoding

  • Sixiong Ke,
  • Banghua Yang,
  • Yiyang Qin,
  • Fenqi Rong,
  • Jiayang Zhang,
  • Yanyan Zheng

DOI
https://doi.org/10.1109/tnsre.2024.3499998
Journal volume & issue
Vol. 32
pp. 4131 – 4142

Abstract

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Motor imagery brain-computer interface (MI-BCI) based on non-invasive electroencephalogram (EEG) signals is a typical paradigm of BCI. However, existing decoding methods face significant challenges in terms of signal decoding accuracy, real-time processing, and deployment. To overcome these challenges, we propose FACT-Net, an innovative deep-learning network for the fast and accurate decoding of MI-EEG signals. FACT-Net incorporates a Frequency Adapter (FA) module designed for processing the frequency features of MI-EEG data, as well as a Temporal-Periodicity Inception (TPI) module specifically for handling global periodic signals in MI. To evaluate the proposed model, we conduct the experiments on the cross-day dataset collected from 67 subjects and the BCIC-IV-2a dataset. The FACT-Net achieved an accuracy of 48.32% and 80.67% higher than the state-of-the-art (SOTA) approaches, demonstrating excellent performance in MI decoding. Additionally, it exhibits exceptional memory efficiency and inference time, indicating significant potential for practical applications. We anticipate that FACT-Net will set a new baseline for MI-EEG decoding. The code is available in https://github.com/Ktn1ga/EEG_FACT.

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