A comprehensive research setup for monitoring Alzheimer’s disease using EEG, fNIRS, and Gait analysis

Kim, Minhee; Jang, Sehyeon; Lee, D.Y.; Lee, Seung-Chan; Gwak, Jeonghwan; Jun, Sung Chan; Kim, Jae Gwan

doi:10.1007/s13534-023-00306-7

Cited by 1 publication

(1 citation statement)

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“…The development of these fNIRS devices enables real-time assessment of users’ working memory load in the real world and help to adjust work allocation for users to assist them in maintaining the best working state and preventing decision-making errors due to working memory overload. Furthermore, fNIRS, as a promising optical neuroimaging technology, has a wide range of uses other than working memory load measurement, such as clinical diagnosis [ 55 ], rehabilitation help [ 56 ], and emotional monitoring [ 57 ]. With the development of fNIRS technology, it will promote real-time WML classification applications.…”

Section: Discussionmentioning

confidence: 99%

Working memory load recognition with deep learning time series classification

Pang,

Sang,

et al. 2024

Biomed. Opt. Express

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Working memory load (WML) is one of the widely applied signals in the areas of human–machine interaction. The precise evaluation of the WML is crucial for this kind of application. This study aims to propose a deep learning (DL) time series classification (TSC) model for inter-subject WML decoding. We used fNIRS to record the hemodynamic signals of 27 participants during visual working memory tasks. Traditional machine learning and deep time series classification algorithms were respectively used for intra-subject and inter-subject WML decoding from the collected blood oxygen signals. The intra-subject classification accuracy of LDA and SVM were 94.6% and 79.1%. Our proposed TAResnet-BiLSTM model had the highest inter-subject WML decoding accuracy, reaching 92.4%. This study provides a new idea and method for the brain-computer interface application of fNIRS in real-time WML detection.

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Section: Discussionmentioning

confidence: 99%