“Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.”
o Amara Graps, 1995-2004
o 1995 by the Institute of Electrical and Electronics Engineers, Inc
The original version of this work appears in IEEE Computational Science and Engineering, Summer 1995, vol. 2, num. 2, published by the IEEE Computer Society
An excellent tutorial on Wavelet Analysis covering also the basic concepts of mathematical transformations, time-frequency representations and non-stationary signal properties.
By Robi Polikar
Dept. of Electrical and Computer Engineering
Wave_clus is a fast and unsupervised algorithm for spike detection and sorting. Although it gives a first unsupervised solution, this can be further modified according to the experimenter’s preference (semi-automatic sorting).
By Rodrigo Quian Quiroga,
Reader in Bioengineering,
Dept. Engineering. University of Leicester, UK.
Trajectories tCS Neuroimaging Plasticity CPG Biometrics brain to brain interface neuroethics Retinal prosthesis Graph Theory systems-neuroscience music Connectomics human to human interface Exoskeleton Brain-inspired Computing transcranial direct current stimulation Neuro-inspired Computing TMS Neural coding Deep Brain Stimulation LFP Noise Sparse neurons Wavelets Computational Neuroscience neuroprosthetics EEG Machine Learning Bionics neural recordings Information Theory Dimensionality Reduction Clustering Neural Networks Modelling Statistical Analysis Neurophysiology Spike Sorting Brain Research Brain Interfaces