Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects | Zendy

Nikolay V. Manyakov | Zendy; Nikolay Chumerin | Zendy; Adrien Combaz | Zendy; Marc M. Van Hulle | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects

Author(s) -

Nikolay V. Manyakov,

Nikolay Chumerin,

Adrien Combaz,

Marc M. Van Hulle

Publication year - 2011

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2011/519868

Subject(s) - brain–computer interface , classifier (uml) , typing , amyotrophic lateral sclerosis , physical medicine and rehabilitation , computer science , speech recognition , logistic regression , artificial intelligence , medicine , pattern recognition (psychology) , psychology , machine learning , electroencephalography , neuroscience , pathology , disease

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 classifier used. We considered 7 types of classifiers, linear as well as nonlinear ones, and found that, overall, one type of linear classifier yielded a higher classification accuracy. In addition to the selection of the classifier, we also suggest and discuss a number of recommendations to be considered when building a P300-based typing system for disabled subjects.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research