Relative power analysis of electroencephalograph rhythms in elderly with mild cognitive impairment

Ye, Jianpeng; Lan, Yanmei; Wang, Rencheng; Yang, Yiyong

doi:10.1109/cisp-bmei.2017.8302233

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…Using EEG modality, power-based neuromarkers have also been used for MCI classification. Ye et al [21] conducted a series of tasks to distinguish the healthy controls and MCI patients using a 64-channel EEG recording system. Their experiments included 22 participants, half of which were MCI subjects.…”

Section: Frequency Domain Neuromarkersmentioning

confidence: 99%

“…Several effective neuromarkers have been tested to predict the MCI and diagnose the AD [16,17,18,19] . For example in the frequency domain, the power spectral density (PSD) and related methods have long been one of the most frequently used techniques to explore effective neuromarkers in EEG/MEG research [20,21]. In the time domain, the analysis of functional connectivity has been especially useful in the study of unusual changes in region(s) of interest and its communication [23].…”

Section: Introductionmentioning

confidence: 99%

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M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer's Disease: A Review From the ML Perspective

Yang

Sánchez-Bornot

Wong‐Lin

et al. 2019

IEEE Trans. Biomed. Eng.

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This work reviews the state-of-the-art neuromarkers development for the prognosis of Alzheimer's diseases (AD) and mild cognitive impairment (MCI). The first part of this study is devoted to reviewing the recently emerged machine learning (ML) algorithms based on electroencephalography (EEG) and magnetoencephalography (MEG) modalities. In particular, the methods are categorized by different types of neuromarkers. The second part of the review is dedicated to a series of investigations that further highlight the differences between these two modalities. Firstly, several source reconstruction methods are reviewed and their source-level performances explored, followed by an objective comparison between EEG and MEG from multiple perspectives. Finally, a number of the most recent reports on classification of MCI/AD during resting state using EEG/MEG are documented to show the up-to-date performance for this wellrecognized data collecting scenario. It is noticed that the MEG modality may be particularly effective in distinguishing between subjects with MCI and healthy controls, a high classification accuracy of more than 98% was reported recently; whereas the EEG seems to be performing well in classifying AD and healthy subjects, which also reached around 98% of the accuracy. A number of influential factors have also been raised and suggested for careful considerations while evaluating the ML-based diagnosis systems in the real-world scenarios.

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Section: Frequency Domain Neuromarkersmentioning

confidence: 99%