MACRO: Multi-Attention Convolutional Recurrent Model for Subject-Independent ERP Detection

• Published in: IEEE signal processing letters Vol. 28; pp. 1505 - 1509
• Main Authors: Lan, Zhen, Yan, Chao, Li, Zixing, Tang, Dengqing, Xiang, Xiaojia
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; attention mechanism; Brain modeling; Convolution; deep learning; EEG; Electroencephalography; ERP detection; Feature extraction; Human-computer interface; Integrated circuits; Kernel; Signal to noise ratio; subject-independent; Training
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2021.3095761

Due to the low signal-to-noise ratio, limited training samples, and large inter-subject variabilities in electroencephalogram (EEG) signals, developing a subject-independent brain-computer interface (BCI) system used for new users without any calibration is still challenging. In this letter, we propose a novel Multi-Attention Convolutional Recurrent mOdel (MACRO) for EEG-based event-related potential (ERP) detection in the subject-independent scenario. Specifically, the convolutional recurrent network is designed to capture the spatial-temporal features, while the multi-attention mechanism is integrated to focus on the most discriminative channels and temporal periods of EEG signals. Comprehensive experiments conducted on a benchmark dataset for RSVP-based BCIs show that our method achieves the best performance compared with the five state-of-the-art baseline methods. This result indicates that our method is able to extract the underlying subject-invariant EEG features and generalize to unseen subjects. Finally, the ablation studies verify the effectiveness of the designed multi-attention mechanism in MACRO for EEG-based ERP detection.