alexandrebarachant/muse-lsl: Muse LSL v2.1 with adaptive timestamp correction, hcitool workaround, configurable device presets, and more bugfixes

Morrison, Dano; barachant, alexandre; Kowaleski, Jason; hubertjbanville,; Chevallier, Sylvain; Shaked, U.; xloem,; Bjäreholt, Erik; Tresols, Juan Jesús Torre; Banville, Hubert; Jawhar, Sami; pdpino,; stepheninx,; Zhao, Taine

doi:10.5281/zenodo.4540829

Cited by 1 publication

(1 citation statement)

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“…We focus our research on retail wearable headsets to capture EEG shortly in home-based environments, considering substantial environmental electromagnetic noise and no clinical experimental experience of the target older adult users. The wearable EEG headbands have already been proven satisfactory in academic research (Barachant et al, 2019;Rutkowski et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

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

Rutkowski

Abe

Komendziński

et al. 2023

Front. Hum. Neurosci.

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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.

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Section: Introductionmentioning

confidence: 99%