Cross-cultural Evaluation of Dementia Passive BCI Neuro-biomarker Candidates

Rutkowski, Tomasz M.; Narebski, Stanislaw; Bekier, Piotr; Komendziński, Tomasz; Sugimoto, Hikaru; Otake-Matsuura, Mihoko

doi:10.1109/scisisis55246.2022.10002105

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…We collect EEG data in the current study using a Unicorn EEG headset by g.tec Medical Engineering, Austria. The Unicorn EEG headset has already been proven a reliable experimental device, compared to other available wearables, in our previously published studies (Rutkowski et al, 2022c). For the initial investigation, we use eight EEG channels uniformly covering the human scalp at the standard locations of (Fz, C3, Cz, C4, Pz, PO7, Cz, and PO8).…”

Section: Eeg Capturementioning

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: Eeg Capturementioning

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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show abstract

“…Our study involved collecting EEG data using the Unicorn EEG headset from g.tec Medical Engineering, a reputable company based in Austria. Our prior research has demonstrated the reliability of this device compared to other available wearables, as reported in publications by Rutkowski et al (2022cRutkowski et al ( , 2023a. The preliminary investigation utilized eight EEG channels that uniformly cover the human scalp at standard locations such as Fz, C3, Cz, C4, Pz, PO7, Oz, and PO8.…”

Section: Eeg Experimentsmentioning

confidence: 97%

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm

Rutkowski,

Komendziński,

Otake-Matsuura

2024

Front. Aging Neurosci.

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IntroductionThe main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuroscience. Our ultimate goal is to utilize this approach to predict the early signs of mild cognitive impairment (MCI) in healthy elderly individuals, which could potentially lead to dementia. The advancements in health neuroscience research have revealed that affective reminiscence stimulation is an effective method for developing EEG-based neuro-biomarkers that can detect the signs of MCI.MethodsWe use topological data analysis (TDA) on multivariate EEG data to extract features that can be used for unsupervised clustering, subsequent machine learning-based classification, and cognitive score regression. We perform EEG experiments to evaluate conscious awareness in affective reminiscent photography settings.ResultsWe use EEG and interior photography to distinguish between healthy cognitive aging and MCI. Our clustering UMAP and random forest application accurately predict MCI stage and MoCA scores.DiscussionOur team has successfully implemented TDA feature extraction, MCI classification, and an initial regression of MoCA scores. However, our study has certain limitations due to a small sample size of only 23 participants and an unbalanced class distribution. To enhance the accuracy and validity of our results, future research should focus on expanding the sample size, ensuring gender balance, and extending the study to a cross-cultural context.

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Mild Cognitive Impairment Detection with Machine Learning and Topological Data Analysis Applied to EEG Time-series in Facial Emotion Oddball Paradigm

Rutkowski,

Abe,

Sugimoto

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Cross-cultural Evaluation of Dementia Passive BCI Neuro-biomarker Candidates

Cited by 5 publications

References 15 publications

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

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

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm

Mild Cognitive Impairment Detection with Machine Learning and Topological Data Analysis Applied to EEG Time-series in Facial Emotion Oddball Paradigm

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