Resting-State EEG Power Spectral Density Analysis Between Healthy and Cognitively Impaired Subjects

<b>Background/Objectives</b>: This study evaluates the potential of electroencephalography (EEG) as a noninvasive tool for distinguishing between healthy individuals (<i>n</i> = 79), those with mild cognitive impairment (MCI; <i>n</i> = 36), and dementia patients (<i>n</i> = 7). <b>Methods</b>: Using a 14-channel Emotiv EPOC-X headset, we analyzed power spectral density during a 2-min eyes-closed resting state. <b>Results</b>: Our results demonstrated that while EEG effectively differentiated dementia patients from healthy controls, it did not show significant differences between MCI and healthy controls. This indicates that EEG holds promise for identifying advanced cognitive decline but faces challenges in early-stage detection. <b>Conclusions</b>: The study contributes to the growing body of literature by highlighting EEG's potential as a cost-effective alternative to invasive diagnostic methods while also identifying the need for larger sample sizes and task-oriented approaches to improve its diagnostic precision.

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Publication Details

Journal
Brain Sciences
Published
2025-02-10
DOI
https://doi.org/10.3390/brainsci15020173
Citations
9
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
12.88

Funders

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article

Resting-State EEG Power Spectral Density Analysis Between Healthy and Cognitively Impaired Subjects

Hariom Yadav, Kenneth S. Walters, Shannon Schueren, Rohit Shukla et al.
9 citations
Brain Sciences
EEG and Brain-Computer Interfaces
12.88
article

Resting-State EEG Power Spectral Density Analysis Between Healthy and Cognitively Impaired Subjects

Hariom Yadav, Kenneth S. Walters, Shannon Schueren, Rohit Shukla, Shalini Jain, Nathan D. Schilaty, Vivek Kumar
article en
9 citations

Abstract

<b>Background/Objectives</b>: This study evaluates the potential of electroencephalography (EEG) as a noninvasive tool for distinguishing between healthy individuals (<i>n</i> = 79), those with mild cognitive impairment (MCI; <i>n</i> = 36), and dementia patients (<i>n</i> = 7). <b>Methods</b>: Using a 14-channel Emotiv EPOC-X headset, we analyzed power spectral density during a 2-min eyes-closed resting state. <b>Results</b>: Our results demonstrated that while EEG effectively differentiated dementia patients from healthy controls, it did not show significant differences between MCI and healthy controls. This indicates that EEG holds promise for identifying advanced cognitive decline but faces challenges in early-stage detection. <b>Conclusions</b>: The study contributes to the growing body of literature by highlighting EEG's potential as a cost-effective alternative to invasive diagnostic methods while also identifying the need for larger sample sizes and task-oriented approaches to improve its diagnostic precision.

Brain SciencesVol. 15(2)
University of South Florida (US)
Florida Department of Health, National Institutes of Health
Openalex Percentile: Top 1%
EEG and Brain-Computer Interfaces
12.88
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Resting-State EEG Power Spectral Density Analysis Between Healthy and Cognitively Impaired Subjects — Hariom Yadav, Kenneth S. Walters, et al. · Brain Sciences (2025) | TGRS Research Map | TGRS