AUTHOR=Rutkowski Tomasz M. , Abe Masato S. , Komendzinski Tomasz , Sugimoto Hikaru , Narebski Stanislaw , Otake-Matsuura Mihoko 

TITLE=Machine learning approach for early onset dementia neurobiomarker using EEG network topology features

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2023.1155194

DOI=10.3389/fnhum.2023.1155194

ISSN=1662-5161

ABSTRACT=Modern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called ‘AI for social good’ domain contributes to the well-being improvement of individuals with a disability.  Using digital health technologies,  home-based self-diagnostics, or cognitive decline managing approaches with neurobiomarker feedback may be helpful for the elderly to remain independent and improve their well-being.  We report research results related to early onset dementia neuro-biomarkers to scrutinize cognitive-behavioral intervention management and digital non-pharmacological therapies. We present an empirical task in the EEG-based passive brain-computer interface application framework to assess working memory decline for forecasting a mild cognitive impairment. We report findings from an elderly volunteer pilot study group in Poland for a cognitive decline prediction. We utilize two emotional working memory tasks by analyzing EEG responses to facial emotions reproduced in short videos. A reminiscent interior image oddball task is also employed to validate the proposed methodology further. The EEG responses are analyzed in a framework of a network neuroscience technique applied to EEG time series for evaluation and to confirm the initial hypothesis of possible ML application modeling mild cognitive impairment prediction. The proposed three experimental tasks in the current pilot study showcase the critical utilization of artificial intelligence for early-onset dementia prognosis in the elderly.