Linear and nonlinear methods for brain-computer interfaces

Author

Müller, Klaus-Robert ; Anderson, Charles W. ; Birch, Gary E.

Author_Institution

Fraunhofer FIRST.IDA, Berlin, Germany

Volume

11

Issue

2

fYear

2003

fDate

6/1/2003 12:00:00 AM

Firstpage

165

Lastpage

169

Abstract

At the recent Second International Meeting on Brain-Computer Interfaces (BCIs) held in June 2002 in Rensselaerville, NY, a formal debate was held on the pros and cons of linear and nonlinear methods in BCI research. Specific examples applying EEG data sets to linear and nonlinear methods are given and an overview of the various pros and cons of each approach is summarized. Overall, it was agreed that simplicity is generally best and, therefore, the use of linear methods is recommended wherever possible. It was also agreed that nonlinear methods in some applications can provide better results, particularly with complex and/or other very large data sets.

Keywords

electroencephalography; handicapped aids; learning automata; mathematical programming; reviews; EEG data sets; Fisher´s disciriminant; Second International Meeting on Brain-Computer Interfaces; feature spaces; linear methods; mathematical programming machines; nonlinear methods; support vector machines; Brain computer interfaces; Brain modeling; Computer science; Councils; Counting circuits; Electroencephalography; Feature extraction; Mathematical programming; Signal processing; Support vector machines; Algorithms; Brain; Electroencephalography; Humans; Linear Models; Models, Neurological; Neural Networks (Computer); Nonlinear Dynamics; Reproducibility of Results; Sensitivity and Specificity; User-Computer Interface;

fLanguage

English

Journal_Title

Neural Systems and Rehabilitation Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1534-4320

Type

jour

DOI

10.1109/TNSRE.2003.814484

Filename

1214711