Training a subject-independent Electroencephalography (EEG) classification model is challenging since there are large variations in EEG signals between different subjects. To this end, existing works adopt the subject-dependent training scheme to reduce the individual variations, but training one model per each subject raises expensive costs, especially when the number of subjects is large. In this work, we aim to learn a subject-independent EEG classification model that predicts target labels independent of subjects, which avoids the cost issue. Specifically, we prevent the model from learning subject dependency via minimizing the mutual information between target and subject labels. Our model consists of a feature embedding module, followed by two branches for target and subject label prediction. The subject prediction module is trained adversarially against the feature embedding module, which encourages the feature representation to be encoded invariant to the subjects. To evaluate our method, we conduct experiments on the EEG-based drowsy driving detection task, requiring consistent performances among different subjects to be adapted in real-world applications. Through the analysis on SEED-VIG dataset, we demonstrate that our method achieves meaningful performance in terms of both accuracy and individual differences.
|Title of host publication||9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2021 Feb 22|
|Event||9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 - Gangwon, Korea, Republic of|
Duration: 2021 Feb 22 → 2021 Feb 24
|Name||9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021|
|Conference||9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021|
|Country/Territory||Korea, Republic of|
|Period||21/2/22 → 21/2/24|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (Development of BCI based Brain and Cognitive Computing Technology for Recognizing Users Intentions using Deep Learning) under Grant 2017-0-00451 and (Artificial Intelligence Graduate School Program (YONSEI UNIVERSITY)) under Grant 2020-0-01361. *Corresponding author.
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing