Comparison of Linear Classification Methods for P300 Brain-computer Interface on Disabled Subjects
نویسندگان
چکیده
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.
منابع مشابه
Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects
We report on tests with a mind typing paradigm based on a P300 brain-computer interface (BCI) on a group of amyotrophic lateral sclerosis (ALS), middle cerebral artery (MCA) stroke, and subarachnoid hemorrhage (SAH) patients, suffering from motor and speech disabilities. We investigate the achieved typing accuracy given the individual patient's disorder, and how it correlates with the type of c...
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