Spatial permutation entropy distinguishes resting brain states

Bruno R.R. Boaretto, Roberto C. Budzinski, Kalel L. Rossi, Cristina Masoller and Elbert E.N. Macau

Chaos, Solitons & Fractals, 2023, vol. 171, issue C

Abstract: We use ordinal analysis and spatial permutation entropy to distinguish between eyes-open and eyes-closed resting brain states. To do so, we analyze EEG data recorded with 64 electrodes from 109 healthy subjects, under two one-minute baseline runs: One with eyes open, and one with eyes closed. We use spatial ordinal analysis to distinguish between these states, where the permutation entropy is evaluated considering the spatial distribution of electrodes for each time instant. We analyze both raw and post-processed data considering only the alpha-band frequency (8–12Hz) which is known to be important for resting states in the brain. We conclude that spatial ordinal analysis captures information about correlations between time series in different electrodes. This allows the discrimination of eyes closed and eyes open resting states in both raw and filtered data. Filtering the data only amplifies the distinction between states. Importantly, our approach does not require EEG signal pre-processing, which is an advantage for real-time applications, such as brain-computer interfaces.

Keywords: Time series analysis; Ordinal analysis; EEG (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:171:y:2023:i:c:s0960077923003545

DOI: 10.1016/j.chaos.2023.113453

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