Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator; Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.
|Title of host publication
|2022 26th International Conference on Pattern Recognition, ICPR 2022
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2022
|26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: 2022 Aug 21 → 2022 Aug 25
|Proceedings - International Conference on Pattern Recognition
|26th International Conference on Pattern Recognition, ICPR 2022
|22/8/21 → 22/8/25
Bibliographical noteFunding Information:
This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development of Non-Invasive Integrated BCI SW Platform to Control Home Appliances and External Devices by User’s Thought via AR/VR Interface; No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University).
© 2022 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition