Decomposition of EEG Signals for Multichannel Neural Activity Analysis in Animal Experiments

We describe in this paper an advanced protocol for the discrimination and the classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse decomposition was used to serarate the linearly independent signals underlying sensory information in cortical spike firing patterns. We introduce some modifications in the the IDE algorithm to take into account prior knowledge on the spike waveforms. We have investigated motor cortex responses recorded during movement in freely moving rats to provide evidence for the relationship between these patterns and special behavioral task. Key words: Sparse decomposition, classification, semi-supervised learning, Atomic Decomposition, neuronal spikes detection

• Type: Conference
• Year: 2010
• Where: ICA
• Authors: Vincent Vigneron, Hsin Chen, Yen-Tai Chen, Hsin-Yi Lai, You-Yin Chen
• Keywords: ICA 2010, Neuronal Spike Waveforms, Neuronal Spikes, Signal Processing, Spike Waveforms