Publication details
Complexity and information-based analysis of th electroencephalogram (EEG) signals in standing, walking, and walking with a brain-computer interface
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Periodical |
| Magazine / Source | Fractals |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.worldscientific.com/doi/epdf/10.1142/S0218348X22500414 |
| Doi | http://dx.doi.org/10.1142/S0218348X22500414 |
| Keywords | EEG Signals; Complexity; Information; Fractal Theory; Shannon Entropy |
| Attached files | |
| Description | In this paper, we analyzed the variations in brain activation between different activities. SinceElectroencephalogram (EEG) signals as an indicator of brain activation contain information andhave complex structures, we employed complexityand information-based analysis. Specifically,we used fractal theory and Shannon entropy for our analysis. Eight subjects performed threedifferent activities (standing, walking, and walking with a brain–computer interface) whiletheir EEG signals were recorded. Based on the results, the complexity and information contentof EEG signals have the greatest and smallest values in walking and standing, respectively.Complexity and information-based analysis can be applied to analyze the activations of otherorgans in different conditions. |