Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 2; pp. 739 - 749
• Main Authors: Jeon, Eunjin, Ko, Wonjun, Yoon, Jee Seok, Suk, Heung-Il
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Adaptation; Algorithms; Brain modeling; Brain-Computer Interfaces; Brain–computer interface (BCI); Calibration; Decoding; Deep learning; domain adaptation; Domains; EEG; electroencephalogram; Electroencephalography; Electroencephalography - methods; Feature extraction; Human-computer interface; Humans; Information theory; Invariants; Machine Learning; motor imagery; Mutual information; Neural Networks, Computer; Representations; subject-independent; Training; Transfer learning

In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific...