Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 1047 - 1057
• Main Authors: Gao, Wei, Yu, Tianyou, Yu, Jin-Gang, Gu, Zhenghui, Li, Kendi, Huang, Yong, Yu, Zhu Liang, Li, Yuanqing
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Amplifiers; Artificial neural networks; big data; Brain; Brain modeling; Brain-computer interface (BCI); Calibration; Computer applications; convolutional neural network (CNN); cross-subject; Data models; EEG; Electroencephalography; electroencephalography (EEG); Event-related potentials; Feature extraction; Human-computer interface; Implants; Interfaces; invariant pattern learning; Invariants; Learning; Neural networks; P300; Task analysis; Training
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2021.3083548

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject. In this study, we propose an invariant pattern learning method based on a convolutional neural network (CNN) and big EEG data for subject-independent P300 BCIs. The CNN was trained using EEG data from a large number of subjects, allowing it to extract subject-independent features and make predictions for new users. We collected EEG data from 200 subjects in a P300-based spelling task using two different types of amplifiers. The offline analysis showed that almost all subjects obtained significant cross-subject and cross-amplifier effects, with an average accuracy of more than 80%. Furthermore, more than half of the subjects achieved accuracies above 85%. These results indicated that our method was effective for building a subject-independent P300 BCI, with which more than 50% of users could achieve high accuracies without subject-specific calibration.