This paper tackles the problem of subject adaptive EEG-based visual recognition. Its goal is to accurately predict the categories of visual stimuli based on EEG signals with only a handful of samples for the target subject during training. The key challenge is how to appropriately transfer the knowledge obtained from abundant data of source subjects to the subject of interest. To this end, we introduce a novel method that allows for learning subject-independent representation by increasing the similarity of features sharing the same class but coming from different subjects. With the dedicated sampling principle, our model effectively captures the common knowledge shared across different subjects, thereby achieving promising performance for the target subject even under harsh problem settings with limited data. Specifically, on the EEG-ImageNet40 benchmark, our model records the top-1 / top-3 test accuracy of 72.6% / 91.6% when using only five EEG samples per class for the target subject. Our code is available at https://github.com/DeepBCI/Deep-BCI.
|Title of host publication||10th International Winter Conference on Brain-Computer Interface, BCI 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2022|
|Event||10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of|
Duration: 2022 Feb 21 → 2022 Feb 23
|Name||International Winter Conference on Brain-Computer Interface, BCI|
|Conference||10th International Winter Conference on Brain-Computer Interface, BCI 2022|
|Country/Territory||Korea, Republic of|
|Period||22/2/21 → 22/2/23|
Bibliographical notePublisher Copyright:
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing