Complexity and non-predictability in neurodynamic: gender-specific EEG dynamics uncovered via hidden markov models.

One area of interest in neuroscience is the study of differences between male and female brains, encompassing structural, physiological, and neural activity, as well as their implications for behavioral traits and functional capabilities. In this study, we investigate the differences in the complexity of EEG signals between men and women and propose hidden Markov model (HMM) method for measuring complexity which significantly improves the accuracy of gender-based classification compared to conventional signal complexity measures. Using this method to measure complexity of signal, we enhanced the results by reaching to 86% decoding accuracy. Additionally, we demonstrated that the observed effect is particularly dominant in the parietal, frontal and central regions of the brain. Through signal filtering, we observed that differences in signal complexity between men and women are present across most of frequency bands with a high rate of enhancement. It is also noteworthy that the level of complexity in women's brain activity is higher than in men's. The results of HMM method showed higher classification accuracy across most frequency bands compared to conventional methods for measuring signal complexity and nonlinearity, such as entropy, Lyapunov and Hurst exponent. Importantly, the performance improvement rate was significantly higher than that of other conventional methods. Additionally, our finding of higher signal complexity in female was entirely consistent with previous studies. Overall, the results demonstrated that using a Hidden Markov Model can more effectively extract signal complexity, significantly enhancing the accuracy of EEG-based gender classification.