The ASCENT EEGLAB plugin computes entropy and complexity measures from multidimensional M/EEG data (or any other time series).

Measures supported

Uniscale measures:

• Sample entropy (SampEn)
• Extrema-segmented entropy analysis of time series (ExSEnt; improved SampEn from Kamali 2025)
• Fuzzy entropy (FuzzEn)
• Fractal Dimension/Volatility (FracDim)

Multiscale measures:

• Multiscale entropy (MSE; enhanced version of Costa 2002)
• Modified Multiscale entropy (mMSE; enhanced version of Kloosterman 2020 and Kosciessa 2020)
• Multiscale fuzzy entropy (MFE; enhanced version of Azami 2017)
• Refined composite multiscale fuzzy entropy (RCMFE; enhanced version of Azami 2017)

All algorithms were modified to significantly increase computation speed while preserving the integrity of the complexity estimates via vectorization, matrix operations, parallel computing, and blockwise bounded-memory blockwise distance calculations to avoid full pairwise matrix allocation.

Requirements

• MATLAB
• EEGLAB

Please cite

https://psyarxiv.com/xwmyk/

Illustration

We computed all of ASCENT's measures for two conditions of 64-channel Biosemi data: eyes-open vs eyes-closed resting state (N = 40). We then compared conditions with paired permutation t-tests (2,000 permutations; α = 0.05) and cluster-based correction for the family-wise error (FWE; Type 1 error), highlighting the significant spatial or spatiotemporal clusters (script: ascent_group_analysis.m).

For the multiscale measures, group analysis was performed with both the standard deviation and the mean as the coarse-graining method.

Time to compute everything with 32 GB of RAM and 10 cores with parallel computing: ~11 hours.

Uniscale measures

Multiscale measures

Standard deviation coarse-graining:

Mean coarse-graining: