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
• ISSN: 2073-431X; 2073-431X
• DOI: 10.3390/computers13070158

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 inter-subject variability in EEG data, complicating the choice of the best classifier for different individuals over time. There is a keen need for an automatic approach to selecting a personalized classifier suited to an individual’s current needs. To this end, we have developed a systematic methodology for individual classifier selection, wherein the structural characteristics of an EEG dataset are used to predict a classifier that will perform with high accuracy. The method was evaluated using motor imagery EEG data from Physionet. We confirmed that our approach could consistently predict a classifier whose performance was no worse than the single-best-performing classifier across the participants. Furthermore, Kullback–Leibler divergences between reference distributions and signal amplitude and class label distributions emerged as the most important characteristics for classifier prediction, suggesting that classifier choice depends heavily on the morphology of signal amplitude densities and the degree of class imbalance in an EEG dataset.