On the use of spiking neural network for EEG classification

This paper presents a new classification technique of continuous EEG recordings, based on a network of spiking neurons. Human EEG signals published on the BCI Competition website were used for the study. The signals were pre-processed using Wavelet Transform to remove the noise and to extract the low frequency content. Analysis of the signals was performed on the ensemble EEG and the task of the neural network was to identify the P300 component in the signal. The network employed leaky-integrate-and-fire (LIF) neurons as nodes in a multi-layered structure. The method involved formation of multiple weak classifiers to perform voting. Collective results are used for final classification. Results have shown the method to perform better than a genetic algorithm approach to the same problem.

• Type: Journal
• Year: 2008
• Where: KES
• Authors: Piyush Goel, Honghai Liu, David J. Brown, Avijit Datta
• Keywords: BCI Competition Website, Continuous Eeg Recordings, Information Technology, KES 2008, Low Frequency Content