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

Frontiers in Human Neuroscience

Human Neuroscience

Tomasz Komendzinski¹  Stanislaw Narebski¹  Mihoko Otake-Matsuura²  Hikaru Sugimoto²  Masato S. Abe³  Tomasz M. Rutkowski⁴ 

[1] Nicolaus Copernicus University, Toruń, Poland;RIKEN Center for Advanced Intelligence Project, Tokyo, Japan;RIKEN Center for Advanced Intelligence Project, Tokyo, Japan;Doshisha University, Kyoto, Japan;RIKEN Center for Advanced Intelligence Project, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;Nicolaus Copernicus University, Toruń, Poland;

关键词: EEG;    dementia;    biomarker;    mild cognitive impairment;    machine learning;    artificial intelligence;    prevention;    network neuroscience;   

DOI  :  10.3389/fnhum.2023.1155194

 received in 2023-02-13, accepted in 2023-05-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionModern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called “AI for social good” domain contributes to improving the well-being of individuals with a disability. Using digital health technologies, home-based self-diagnostics, or cognitive decline managing approaches with neuro-biomarker feedback may be helpful for older adults to remain independent and improve their wellbeing. We report research results on early-onset dementia neuro-biomarkers to scrutinize cognitive-behavioral intervention management and digital non-pharmacological therapies.MethodsWe 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. 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.ResultsWe report findings from a 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.DiscussionThe proposed three experimental tasks in the current pilot study showcase the critical utilization of artificial intelligence for early-onset dementia prognosis in older adults.

【 授权许可】

Unknown   
Copyright © 2023 Rutkowski, Abe, Komendzinski, Sugimoto, Narebski and Otake-Matsuura.