title: Using time-dependent neural networks for EEG classification.
abstract: This paper compares two different topologies of neural networks. They are used to classify single trial electroencephalograph (EEG) data from a brain-computer interface (BCI). A short introduction to time series classification is given, and the used classifiers are described. Standard multilayer perceptrons (MLPs) are used as a standard method for classification. They are compared to finite impulse response (FIR) MLPs, which use FIR filters instead of static weights to allow temporal processing inside the classifier. A theoretical comparison of the two architectures is presented. The results of a BCI experiment with three different subjects are given and discussed. These results demonstrate the higher performance of the FIR MLP compared with the standard MLP.
authors.affiliation: Department of Medical Informatics, Graz Technical University, Austria. hasi@dpmi.tu-graz.ac.at
authors.forename: E
authors.initials: E
authors.lastname: Haselsteiner
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
mesh_terms: D002540:Cerebral Cortex / Q000502:physiology; D004569:Electroencephalography; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D014584:User-Computer Interface