Explaining Convolutional Neural Networks for EEG-based Brain-Computer Interface Using Influence Functions
- Resource Type
- Conference
- Authors
- Park, Hoonseok; Park, Donghyun; Kim, Sangyeon; Choo, Sanghyun; Nam, Chang. S.; Lee, Sangwon; Jung, Jae-Yoon
- Source
- 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :4436-4440 Oct, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Predictive models
Electroencephalography
Brain-computer interfaces
Convolutional neural networks
Task analysis
Cybernetics
Electroencephalography (EEG)
Brain-computer interface (BCI)
Machine learning interpretation
Motor imagery classification
Input perturbations
Influence scores
- Language
- ISSN
- 2577-1655
Although the high performance of the convolutional neural networks (CNNs) for brain-computer interface (BCI) tasks based on raw electroencephalography (EEG) signals, the explanation of the prediction result remains challenging owing to their complex structure and numerous parameters. We propose a novel framework for explaining CNNs for EEG-based BCI tasks by using the perturbation-based influence scores. The method supports the interpretation of CNN classification for EEG signals at both the example-level and the feature-level. The experiments on the BCIC III-IVa dataset demonstrate that the proposed method is effective for not only the interpretation of the predictive models, but also for the improvement of the classification accuracy.