Personalized Classifier Selection for EEG-Based BCIs

• Published in: Computers (Basel) Vol. 13; no. 7; p. 158
• Main Authors: Rahimipour Anaraki, Javad, Kolokolova, Antonina, Chau, Tom
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Accuracy; algorithm portfolio; Algorithms; Amplitudes; Artificial intelligence; Automation; brain–computer interface; Classification; Comparative analysis; Computational linguistics; Customization; Datasets; EEG data; Electroencephalography; Evaluation; Human-computer interaction; Human-computer interface; Language processing; Machine learning; Natural language interfaces; Optimization techniques; Performance prediction; Regression analysis; Signal classification; Canada

The most important component of an Electroencephalogram (EEG) Brain–Computer Interface (BCI) is its classifier, which translates EEG signals in real time into meaningful commands. The accuracy and speed of the classifier determine the utility of the BCI. However, there is significant intra- and inte...