Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease Ruiz de Miras, Juan Fractal dimension Box counting EEG Parkinson’s disease Neurodegeneration Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD).We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD 2023-09-25T06:58:31Z 2023-09-25T06:58:31Z 2023-07-02 journal article Ruiz de Miras, J.; Derchi, C.-C.; Atzori, T.; Mazza, A.; Arcuri, P.; Salvatore, A.; Navarro, J.; Saibene, F.L.; Meloni, M.; Comanducci, A. Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson’s Disease. Entropy 2023, 25, 1017. [https://doi.org/10.3390/e25071017] https://hdl.handle.net/10481/84604 10.3390/ e25071017 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI