Molcho Lior, Maimon Neta B, Regev-Plotnik Noa, Rabinowicz Sarit, Intrator Nathan, Sasson Ady
Neurosteer Inc., New York, NY, United States.
The School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.
Front Aging Neurosci. 2022 May 30;14:773692. doi: 10.3389/fnagi.2022.773692. eCollection 2022.
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.
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.
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.
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.
NIH 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].
尽管人们努力寻找新的认知生物标志物，但认知能力下降的诊断率仍然很低。基于机器学习（ML）的脑电图（EEG）特征可能提供一种非侵入性、低成本的方法来识别认知能力下降。然而，大多数研究使用的是繁琐的多电极系统。本研究旨在评估使用基于机器学习的EEG特征（从单通道EEG中提取并结合听觉认知评估）来评估认知状态的能力。
本研究纳入了不同认知状态的老年参与者（60名）和健康对照者（22名）的数据，在进行单通道EEG记录的同时进行听觉认知评估。使用简易精神状态检查表（MMSE）评分来划分组别，临界值分别为24分和27分。EEG数据处理包括小波包分解和机器学习以提取EEG特征。数据分析包括对几个EEG变量进行Pearson相关性分析和广义线性混合模型分析：δ和θ频段以及三个基于机器学习的EEG特征：VC9、ST4和A0，这些特征先前从不同数据集中提取并显示与认知负荷相关。
MMSE评分与反应时间以及EEG特征A0和ST4显著相关。这些特征在研究组之间也显示出显著差异：A0在MMSE<24分和MMSE≥28分的组之间有区分，此外还能区分年轻参与者和老年组。ST4在MMSE<24分的组与所有其他组（MMSE 24 - 27分、MMSE≥28分和健康年轻组）之间有区分，显示出对认知状态细微变化的敏感性。EEG特征θ、δ、A0和VC9在认知负荷水平较高时活动增加，仅在健康年轻组中出现，表明不同认知状态下年轻和老年参与者之间存在不同的活动模式。与先前报告一致，这种关联在VC9中最为显著，它在所有认知负荷水平之间都有明显区分。
本研究成功证明了使用简单易用的单通道EEG结合听觉认知评估来评估认知状态的能力。设置时间短和新颖的机器学习特征使得能够客观且容易地评估认知状态。未来的研究应探索该工具在表征认知能力下降随时间变化的EEG模式变化方面的潜在用途，以及在每个诊所大规模检测认知能力下降以实现早期干预的可能性。
