Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

The Large Time-Frequency Analysis Toolbox 2.0

The Large Time Frequency Analysis Toolbox (LTFAT) is a modern Octave/Matlab toolbox for time-frequency analysis, synthesis, coefficient manipulation and visualization. It’s purpose is to serve as a tool for achieving new scientific developments as well as an educational tool. The present paper introduces main features of the second major release of the toolbox which includes: generalizations of...

The Linear Time Frequency Analysis Toolbox

The Linear Time Frequency Analysis Toolbox is a Matlab/Octave toolbox for computational time-frequency analysis. It is intended both as an educational and computational tool. The toolbox provides the basic Gabor, Wilson and MDCT transform along with routines for constructing windows ( lter prototypes) and routines for manipulating coe cients. It also provides a bunch of demo scripts devoted eit...

A Matlab Toolbox for Efficient Perfect Reconstruction Time-Frequency Transforms with Log-Frequency Resolution

In this paper, we propose a time-frequency representation where the frequency bins are distributed uniformly in log-frequency and their Q-factors obey a linear function of the bin center frequencies. The latter allows for time-frequency representations where the bandwidths can be e.g. constant on the log-frequency scale (constant Q) or constant on the auditory critical-band scale (smoothly vary...

Electromembrane Processes, a Toolbox for Advanced Separations

Detecting time-dependent coherence between non-stationary electrophysiological signals--a combined statistical and time-frequency approach.

Various time-frequency methods have been used to study time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence estimate using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. The approach is based on averaging over repeat trials. ...