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

Abstract

BackgroundCognitive 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.MethodsThis 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.ResultsMMSE 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.DiscussionThis 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 RegistrationNIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].

Evaluation of Parkinson’s disease early diagnosis using single-channel EEG features and auditory cognitive assessment

“…Results indicate that the Delta band and EEG feature A0 differentiate between the groups: Delta and A0 exhibited lower activity for the PD patients compared to healthy controls. This difference was more pronounced for A0 than Delta, suggesting that A0 may be more sensitive to functional changes, a notion supported by the highest separation demonstrated between groups with different levels of cognitive decline in a previous study ( 28 ). Finally, the L1 biomarker, which was previously shown to correlate with cognitive load ( 29 ), exhibited lower activity in resting state for healthy controls.…”

“…This study included a previously described auditory detection task ( 28 ), an auditory n-back task, and resting state tasks (see Figure 2 ).…”

“…The EEG signal was decomposed into multiple components using harmonic analysis mathematical models ( 9 , 33 ), and ML methods were employed on the components to extract higher-level EEG features. The full technical specifications for signal processing can be found in Molcho et al ( 28 ). In summary, the Neurosteer® signal-processing algorithm analyzes EEG data using a time/frequency wavelet-packet analysis.…”

“…Of particular interest are two biomarkers: features A0 and L1. EEG feature A0, previously identified as a classifier distinguishing cognitive load from rest in healthy subjects, has been recognized as a potent predictor of cognitive decline in individuals with mild-to-moderate impairment ( 28 ). Whereas EEG feature L1 acted as an LDA classifier, which was developed and validated on datasets involving participants undergoing an n-back working memory task ( 29 ).…”

“…It is essential to note that the participants holding a valid F-DOPA test included in this study had no prior diagnosis of cognitive decline and did not report any neuro-cognitive symptoms. We utilized an auditory assessment with musical stimuli that has been previously employed to distinguish between cognitive decline and healthy senior participants ( 28 , 31 ). The objective of the present study was to assess the capability of these features extracted from a single-channel EEG during an auditory cognitive assessment, to differentiate between positive and negative F-DOPA PET results in order to potentially discriminate between PD and non-PD populations.…”