Authors:
Julio Abascal
;
Andoni Arruti
;
José I. Martín
and
Javier Muguerza
Affiliation:
University of the Basque Country (UPV/EHU), Spain
Keyword(s):
Brain-Computer Interface (BCI), Non Intentional Patterns Detection, Electroencephalogram (EEG), Clustering, Supervised Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
This paper presents a two-level hierarchical approach to recognising intentional and non intentional mental tasks on a brain-computer interface. A clustering process is performed at the first recognition level in order to differentiate Non intentional Control state (NC) patterns from Intentional Control (IC) patterns. At the second level, the IC detected patterns are classified by means of supervised learning techniques, applied to the type of movement (left hand, right hand, tongue or foot imagery movement). The objective is to achieve high correct movement recognition scores, with a low percentage of wrong decisions (that is, low false positive rates), to avoid user frustration. Offline evaluation of the proposed prototype shows 84.5% accuracy, with a 6.7% false positive rate.