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Wavelet analysis in neurodynamics
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DOI：10.3367/UFNe.0182.201209a.0905.png)
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Abstract
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Results obtained using continuous and discrete wavelet transforms as applied to problems in neurodynamics are reviewed, with the emphasis on the potential of wavelet analysis for decoding signal information from neural systems and networks. The following areas of application are considered: (1) the microscopic dynamics of single cells and intracellular processes, (2) sensory data processing, (3) the group dynamics of neuronal ensembles, and (4) the macrodynamics of rhythmical brain activity (using multichannel EEG recordings). The detection and classification of various oscillatory patterns of brain electrical activity and the development of continuous wavelet-based brain activity monitoring systems are also discussed as possibilities.
Keywords:
INDEPENDENT COMPONENT ANALYSIS
UNSUPERVISED WAVEFORM CLASSIFICATION
EVENT-RELATED POTENTIALS
MULTI-NEURON RECORDINGS
SOFTWARE-BASED SYSTEM
ON-OFF INTERMITTENCY
ABSENCE SEIZURES
REAL-TIME
THALAMOCORTICAL OSCILLATIONS
OCULAR ARTIFACTS
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