2020/12/21 by Jean Li, Jeremiah D. Deng, Li, Jean +7
Computer Science · Engineering · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Neural dynamics and brain function #Signal Processing (eess.SP) #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2012.11105
11 pages, 6 figures, book chapter to be published by Springer
arxiv created 2020/12/21 · openalex publication_date 2020/12/21 · arxiv updated 2020/12/22 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex prediction of resting-state EEG signals. We have found that the brain connectivity represented by the coherence between certain sensor channels are good predictors of sex.