Identifying an EEG Biomarker for Memory Recall through EEG and EDA in the Presence of Music

IF 13 1区医学 Q1 CLINICAL NEUROLOGY Alzheimer's & Dementia Pub Date : 2025-01-09 DOI:10.1002/alz.085800

Rupak Kumar Das, Arshia A Khan, Nabiha Zainab Imtiaz

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Abstract

Background

There is ample evidence that music can boost brain activity and jog deeply embedded memories. Literature indicates a significant improvement in autobiographical memory (ABM) recall for different individuals during background music sessions. Existing research is based solely on qualitative data, although music has a significant impact on physiological activity. Thus, it's important to explore the connection between memory recall and physiological activities.

Method

To better understand memory recall, the electroencephalogram (EEG) and electrodermal activity (EDA) data were gathered from healthy participants using wearable sensors. Physiological signals such as the electroencephalogram (EEG) and electrodermal activity (EDA) were recorded as quantitative data using various wearable sensors from 40 participants of different age groups while playing different background music sessions. The study involved listening to nine music sessions (three happy, three sad, and three neutral). Immediately after each piece of music, a post-study survey was conducted to gauge if the participants recalled any autobiographical memories. A machine learning algorithm was developed to train a model using features collected from physiological data to determine if the memory recall was successful. The purpose of the study was to identify an EEG biomarker.

Result

The results of the EEG and EDA data analysis revealed that for all four EEG channels, there was a consistent increase in the alpha power (on average 16.2%) during the memory “recall” scenario (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0.0288) compared to the “no-recall” control. There was also a significant surge in the Beta power for two channels (F3: p = 0.0100 and F4: p = 0.0210) but not for the control (F7: p = 0.6792 and F8: p = 0.0814). Additionally, the EDA data analysis revealed significant differences in the phasic standard deviation (p = 0.0260), phasic max (p = 0.0011), phasic energy (p = 0.0478), tonic min (p = 0.0092), tonic standard deviation (p = 0.0171), and phasic energy (p = 0.0478). This implies that the memory recall biomarker is alpha power (8–12 Hz).

Conclusion

The results indicate that the biomarker for memory recall is alpha power (8-12Hz).

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通过脑电图和EDA识别音乐存在下记忆回忆的脑电图生物标志物

有充分的证据表明，音乐可以促进大脑活动，唤醒根深蒂固的记忆。文献表明，不同个体在听背景音乐时，自传体记忆（ABM）的回忆有显著改善。尽管音乐对生理活动有重大影响，但现有的研究仅基于定性数据。因此，探索记忆回忆和生理活动之间的联系是很重要的。方法采用可穿戴式传感器采集健康受试者的脑电图（EEG）和皮电活动（EDA）数据，以更好地了解记忆回忆。在播放不同背景音乐的同时，使用各种可穿戴传感器记录40名不同年龄组的参与者的脑电图（EEG）和皮电活动（EDA）等生理信号。这项研究包括听九段音乐（三段快乐的，三段悲伤的，三段中性的）。在每一段音乐之后，研究人员立即进行了一项研究后调查，以衡量参与者是否回忆起任何自传式记忆。研究人员开发了一种机器学习算法，利用从生理数据中收集的特征来训练一个模型，以确定记忆回忆是否成功。该研究的目的是确定脑电图生物标志物。结果脑电和脑电数据分析结果显示，4个脑电通道在记忆“回忆”场景下，与“不回忆”对照组相比，α功率（平均16.2%）一致增加（F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, F8: p = 0.0288）。两个通道的Beta功率也有显著的激增（F3: p = 0.0100和F4: p = 0.0210），但对照组没有（F7: p = 0.6792和F8: p = 0.0814）。此外，EDA数据分析显示，相位标准差（p = 0.0260）、相位最大值（p = 0.0011）、相位能量（p = 0.0478）、强直最小值（p = 0.0092）、强直标准差（p = 0.0171）和相位能量（p = 0.0478）存在显著差异。这意味着记忆回忆的生物标记是阿尔法功率（8-12赫兹）。结论记忆回忆的生物标志物为α功率（8 ~ 12Hz）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer's & Dementia 医学-临床神经学

CiteScore

14.50

自引率

5.00%

发文量

299

审稿时长

3 months

期刊介绍： Alzheimer's & Dementia is a peer-reviewed journal that aims to bridge knowledge gaps in dementia research by covering the entire spectrum, from basic science to clinical trials to social and behavioral investigations. It provides a platform for rapid communication of new findings and ideas, optimal translation of research into practical applications, increasing knowledge across diverse disciplines for early detection, diagnosis, and intervention, and identifying promising new research directions. In July 2008, Alzheimer's & Dementia was accepted for indexing by MEDLINE, recognizing its scientific merit and contribution to Alzheimer's research.