Authors:
Nikolay V. Manyakov
;
Nikolay Chumerin
;
Adrien Combaz
and
Marc M. Van Hulle
Affiliation:
K. U. Leuven, Belgium
Keyword(s):
Brain-computer interface, P300, Linear classifier, Classification accuracy, Amyotrophic lateral sclerosis, Middle cerebral artery stroke, Subarachnoid hemorrhage.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Systems
;
Sensor Networks
;
Soft Computing
Abstract:
In this paper, we investigate the accuracy of linear classification techniques for a P300 Brain-Computer Interface used in a typing paradigm. Fisher’s Linear Discriminant Analysis (LDA), Bayesian Linear Discriminant Analysis (BLDA), Stepwise Linear Discriminant Analysis (SLDA), linear Support Vector Machine (SVM) and a method based on Feature Extraction (FE) were compared. Experiments were performed on patients suffering from Amyotrophic Lateral Sclerosis (ALS), middle cerebral artery (MCA) stroke and Subarachnoid Hemorrhage (SAH), in on-line and off-line mode. Our results show that BLDA yields a significantly higher accuracy than the other linear techniques we have compared, at least for our group of subjects.