Detection of Cognitive Decline using a Single-Channel EEG with an Interactive Assessment Tool

Abstract

BackgroundCognitive decline remains highly underdiagnosed despite efforts to find novel biomarkers for detection. EEG biomarkers based on machine learning may offer a noninvasive low-coast approach for identifying cognitive decline. However, most studies use multi-electrode systems which are less accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with an interactive assessment tool.MethodsThis pilot study included data collection from 82 participants who performed a cognitive assessment while being recorded with a single-channel EEG system. Seniors in different clinical stages of cognitive decline (healthy to mild dementia) and young healthy participants were included. Seniors’ MMSE scores were used to allocate groups with cutoff scores of 24 and 27. Data analysis included correlation analysis as well as linear mixed model analysis with several EEG variables including frequency bands and three novel cognitive biomarkers previously extracted from a different dataset. ResultsMMSE scores correlated significantly with reaction times, as well as two EEG biomarkers: A0 and ST4. Both biomarkers showed significant separation between study groups: ST4 separated between the healthy senior group and the low-MMSE group. A0 differentiated between the healthy senior group and the other three groups, showing different cognitive patterns between different stages of cognitive decline as well as different patterns between young and senior healthy participants. In the healthy young group, activity of Theta, Delta, A0 and VC9 biomarkers significantly separated between high and low levels of cognitive load, consistent with previous reports. VC9 and Theta showed a finer separation between low cognitive load levels and resting state.ConclusionsThis study successfully demonstrated the ability to assess cognitive states with an easy-to-use portable single-channel EEG device with an interactive cognitive assessment. The short set-up time and novel biomarkers enable objective and easy assessment of cognitive decline. Future studies should explore potential usefulness of this tool in 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 registrationTrial was retrospectively registered in both: NIH Clinical Trials Registry, number NCT04386902, first posted on May 13, 2020 ; Israeli Ministry of Health (MOH) registry number MOH_2019-10-07_007352, first posted on Oct 07, 2019.

Continuous Monitoring of Mental Load During Virtual Simulator Training for Laparoscopic Surgery Reflects Laparoscopic Dexterity: A Comparative Study Using a Novel Wireless Device

“…Delta and VC9 activity decreased with the lower cognitive load that resulted from simulator trial repetition, but the theta band activity did not decrease. As shown in previous studies ( Maimon et al, 2020 ; Molcho et al, 2021 ), VC9 was found to be more sensitive to lower loads than theta and delta, as its activity also showed a significant decrease between the first and third task trials. This further validates VC9 as an effective biological measurement for the assessment of cognitive load while performing laparoscopic tasks using the surgical simulator specifically and suggests that it may provide a measure of cognitive functioning during surgery.…”

“…A single-channel EEG device utilizing decomposition of the EEG signal via harmonic analysis was used to obtain the novel VC9 biomarker as well as the frequently used theta and delta bands. All EEG features (i.e., theta, delta, and VC9) were previously shown to correlate with cognitive load levels ( Antonenko et al, 2010 ; Maimon et al, 2020 ; Bolton et al, 2021 ; Molcho et al, 2021 ). Experiment 1 consisted of three repeating trials on the surgery simulator, and its results showed that participants’ performances improved while VC9 and delta activity decreased.…”

“…These datasets included data about participants undergoing different cognitive and emotional tasks. Specifically, VC9 was found with linear discriminant analysis technique (LDA, Ye and Li, 2005 ) on the 121 BAFs to be the best separator between an auditory detection task (higher cognitive load) and an auditory classification task (lower cognitive load, Molcho et al, 2021 ). VC9 was also recently validated as a cognitive load biomarker using the n -back task, auditory detection task, and interruption task ( Maimon et al, 2020 ; Bolton et al, 2021 ; Molcho et al, 2021 ).…”

“…Specifically, VC9 was found with linear discriminant analysis technique (LDA, Ye and Li, 2005 ) on the 121 BAFs to be the best separator between an auditory detection task (higher cognitive load) and an auditory classification task (lower cognitive load, Molcho et al, 2021 ). VC9 was also recently validated as a cognitive load biomarker using the n -back task, auditory detection task, and interruption task ( Maimon et al, 2020 ; Bolton et al, 2021 ; Molcho et al, 2021 ). It was shown that VC9 activity increased with increasing levels of cognitive load within cognitively healthy participants ( Maimon et al, 2020 ; Bolton et al, 2021 ), and showed higher sensitivity than the theta band ( Maimon et al, 2021 ).…”

“…In addition to known brain oscillations, Neurosteer ® also provided us with a machine-learning-based cognitive load biomarker (i.e., VC9). VC9 activity was previously shown to increase with cognitive load in the standard and most common WM tasks (i.e., the n -back task, Maimon et al, 2020 ), auditory recognition and classification tasks ( Molcho et al, 2021 ), and interruptions paradigm ( Bolton et al, 2021 ). Specifically, Maimon et al (2021) showed that the VC9 biomarker is more sensitive to the finer differences between cognitive loads.…”