Enhance evoked potentials detection using RBF neural networks: Application to brain-computer interface

Brain-Computer Interface (BCI) is a specific type of human-computer interface that stablish the direct communication between human and computers by analyzing brain activities. Oddball paradigms are used in BCI to generate Event-Related Potentials (ERPs), like the P300 response, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300, allows the user to spell characters. The P300 speller is divided into in two classification problems. The first classification is for detect P300 in the electroencephalogram (EEG). The second one for determining the desired symbol by user. A new method for the detection of P300 waves is presented. This model is based on a Radial Basis Function Neural Network (RBFNN). The architecture of the network is adapted to the detection of P300 response in the time domain. Principal Component Analysis (PCA) is used for feature extraction. These models are tested and compared on the Data set III of BCI competition (2004). The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the RBFNN models.

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