Spatial permutation entropy distinguishes resting brain states
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/387639 |
| Acceso en línea: | https://hdl.handle.net/2117/387639 https://dx.doi.org/10.1016/j.chaos.2023.113453 |
| Access Level: | acceso abierto |
| Palabra clave: | Time-series analysis Entropy Electroencephalography Time series analysis Ordinal analysis EEG Sèries temporals -- Anàlisi Entropia Electroencefalografia Àrees temàtiques de la UPC::Matemàtiques i estadística |
| Sumario: | 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 (8–12 Hz) 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. |
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