Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 10; pp. 1 - 11
• Main Authors: Altindis, Fatih, Banerjee, Antara, Phlypo, Ronald, Yilmaz, Bulent, Congedo, Marco
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Alignment; Brain; Brain modeling; Brain-computer interface (BCI); Classification; Computational neuroscience; Computer applications; Computer Science; Covariance matrices; domain adaptation; Electroencephalography; electroencephalography (EEG); Engineering Sciences; Expression vectors; Human-computer interface; Implants; Linear transformations; Machine learning; Manifolds; Riemannian geometry; Training; Transfer learning; Approximate Joint Diagonalization AJD; Transfer learning TL; P300 classification; Brain-Computer Interface BCI; Riemannian Geometry; Tangent Space

This paper presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is ev...