title: EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures.
abstract: Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's "flexibility" and "customizability," namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.
authors.affiliation: Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Viale del Risorgimento, 40136 Bologna, Italy.
authors.forename: Valeria
authors.identifier: 0000-0001-7680-6199
authors.initials: V
authors.lastname: Mondini
authors.affiliation: Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Viale del Risorgimento, 40136 Bologna, Italy.
authors.forename: Anna Lisa
authors.initials: AL
authors.lastname: Mangia
authors.affiliation: Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Viale del Risorgimento, 40136 Bologna, Italy.
authors.forename: Angelo
authors.initials: A
authors.lastname: Cappello
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D005260:Female; D006801:Humans; D007092:Imagination; D008297:Male; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D013997:Time Factors