Classification of EEG signals using the wavelet transform
Signal Processing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Artificial Intelligence
Wavelet-based transformations for nonlinear signal processing
IEEE Transactions on Signal Processing
Simple model of spiking neurons
IEEE Transactions on Neural Networks
A learning based self-organized additive fuzzy clustering method and its application for EEG data
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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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.