Decoding Silent Reading EEG Signals Using Adaptive Feature Graph Convolutional Network

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Decoding Silent Reading EEG Signals Using Adaptive Feature Graph Convolutional Network

Chengfang Li; Gaoyun Fang; Yang Liu; Jing Liu; Liang Song

Decoding silent reading Electroencephalography (EEG) signals is challenging because of its low signal-to-noise ratio. In addition, EEG signals are typically non-Euclidean structured, therefore merely using a two-dimensional matrix to represent the variation of sampling points of each channel in time cannot richly represent the spatial connection between channels. Furthermore, due to the individual differences in EEG signals, a fixed representation cannot adequately represent the temporal and spatial associations between channels in real time. In this letter, we use the feature matrix and its adaptive graph structure to represent each EEG signal. Then, we use them as inputs and propose a novel Adaptive Feature Graph Convolutional Network (AFGCN) to decode the silent reading EEG signals. We classify silent reading EEG signals under different tasks of 16 subjects from two publicly available datasets. The experimental results demonstrate that our proposed method achieves higher decoding accuracy than state-of-the-art EEG classification networks on both datasets. Among them, the highest classification accuracy for the four classes is 83.33%. The study could promote the application and development of BCI technology for silent reading EEG signal decoding. It can also provide an efficient and convenient communication method for patients with language impairment.

SPS on Twitter

  • DEADLINE EXTENDED: The 2023 IEEE International Workshop on Machine Learning for Signal Processing is now accepting…
  • ONE MONTH OUT! We are celebrating the inaugural SPS Day on 2 June, honoring the date the Society was established in…
  • The new SPS Scholarship Program welcomes applications from students interested in pursuing signal processing educat…
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now seeking submissions for a Special…
  • Test your knowledge of signal processing history with our April trivia! Our 75th anniversary celebration continues:…

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel