Single-Channel EEG Features Reveal an Association With Cognitive Decline in Seniors Performing Auditory Cognitive Assessment

Lior Molcho, Neta B Maimon, Noa Regev-Plotnik, Sarit Rabinowicz, Nathan Intrator, Ady Sasson, Lior Molcho, Neta B Maimon, Noa Regev-Plotnik, Sarit Rabinowicz, Nathan Intrator, Ady Sasson

Abstract

Background: Cognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment.

Methods: This study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load.

Results: MMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24-27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load.

Discussion: This study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention.

Trial registration: NIH Clinical Trials Registry [https://ichgcp.net/clinical-trials-registry/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].

Keywords: EEG (electroencephalography); brain activity; cognitive assessment; cognitive decline; cognitive impairment; machine learning (ML); mini-mental state examination (MMSE); wavelet packet.

Conflict of interest statement

LM, NM, and NI have equity interest in Neurosteer, which developed the Neurosteer system. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Molcho, Maimon, Regev-Plotnik, Rabinowicz, Intrator and Sasson.

Figures

FIGURE 1
FIGURE 1
An example of six trials of detection level 1 (Top) and detection level 2 (Bottom). Both examples show a “trumpet block” in which the participant reacts to the trumpet melody. Red icons represent trials in which the participant was required to respond with a click when hearing the melody, indicating a “yes” response.
FIGURE 2
FIGURE 2
Study design and groups at each stage. The study included both seniors and young healthy participants as controls. For the senior participants, an MMSE score was obtained, and division into groups was based on the individual MMSE score.
FIGURE 3
FIGURE 3
Pearson correlation between individual mean reaction times (RTs) and individual MMSE scores, as a function of task level: detection 1 (purple) and detection 2 (red).
FIGURE 4
FIGURE 4
Pearson correlation between individual MMSE scores and (A) EEG frequency bands Delta and Theta, and (B) EEG features A0, ST4, and VC9, as a function of task level: resting state (blue), detection level 1 (purple), and detection level 2 (red).
FIGURE 5
FIGURE 5
The distributions of groups (MMSE (A) Delta and Theta; (B) A0, ST4 and VC9; and (C) reaction times (RTs), as a function of task: resting-state (blue), detection level 1 (purple), and detection level 2 (red).

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