Subject-Independent Wearable P300 Brain-Computer Interface Based on Convolutional Neural Network and Metric Learning

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 3543 - 3553
• Main Authors: Hu, Li, Gao, Wei, Lu, Zilin, Shan, Chun, Ma, Haiwei, Zhang, Wenyu, Li, Yuanqing
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024
• Subjects: Adult; Algorithms; Brain modeling; brain-computer interface (BCI); Brain-Computer Interfaces; Calibration; convolutional neural network (CNN); Electroencephalography; Event-Related Potentials, P300 - physiology; Feature extraction; Female; Graphical user interfaces; Humans; Machine Learning; Male; Measurement; Neural Networks, Computer; P300; Subject-independent; Vectors; wearable; Wearable Electronic Devices; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3457502

The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed. A dataset of electroencephalogram (EEG) signals was constructed from 100 individuals by conducting a P300 speller task with a wearable EEG amplifier. A framework is proposed that initially improves cross- subject consistency of EEG features through a common feature extractor. Subsequently, a simple and compact convolutional neural network (CNN) architecture is employed to learn an embedding sub-space, where the mapped EEG features are maximally separated, while pursuing the minimum distance within the same class and the maximum distance between different classes. Finally, the model's generalization capability was further optimized through fine-tuning. Results: The proposed method significantly boosts the average accuracy of wearable P300 BCI to <inline-formula> <tex-math notation="LaTeX">73.23\pm 7.62 </tex-math></inline-formula>% without calibration and <inline-formula> <tex-math notation="LaTeX">78.75\pm 6.37 </tex-math></inline-formula>% with fine-tuning. The results demonstrate the feasibility and excellent performance of our dataset and framework. A calibration-free wearable P300 BCI system is feasible, suggesting significant potential for practical applications of the wearable P300 BCI system.