Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

IF 2.6 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
BioMed Research International Pub Date : 2025-02-10 eCollection Date: 2025-01-01 DOI:10.1155/bmri/3585125
Luis Alfredo Moctezuma, Marta Molinas, Takashi Abe
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Abstract

Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone to bias. This study was aimed at developing an EEG-based machine learning (ML) model to automatically identify dream and dreamless states in sleep. We extracted features from EEG data using common spatial patterns (CSPs) and the discrete wavelet transform (DWT) and used them to classify EEG signals into dream and dreamless states using ML models. To determine the most informative channels for classification, we used the permutation-based channel selection method and the nondominated sorting genetic algorithm II (NSGA-II). We evaluated our proposal using a public dataset that is part of the DREAM project, which was collected from 58 EEG channels during rapid eye movement (REM) and non-REM sleep, while 28 subjects reported dream or dreamless experiences. We achieved accuracies greater than 0.85 to distinguish dream and dreamless states using CSP-based feature extraction combined with k-nearest neighbors (KNN), as well as through multiple combinations of EEG channels identified by channel selection methods. Our findings suggest that as few as 8-10 EEG channels may be sufficient for dream recognition. Excluding one subject at a time during model training revealed challenges in generalizing the models to unseen subjects. Channel selection methods have proven to be effective in selecting relevant subsets of EEG channels to classify dreams and dreamless experiences. Our results demonstrate the feasibility of automatic dream detection and highlight the need to improve ML generalization.

解梦和无梦睡眠:机器学习分类与最佳脑电图通道。
研究表明,梦在调节情绪处理和记忆巩固方面发挥着作用;脑电图(EEG)对研究它们是有用的,但手工注释费时且容易产生偏差。这项研究旨在开发一种基于脑电图的机器学习(ML)模型,以自动识别睡眠中的有梦和无梦状态。利用共同空间模式(csp)和离散小波变换(DWT)对脑电信号进行特征提取,并利用ML模型将脑电信号分为有梦状态和无梦状态。为了确定最具信息量的分类通道,我们使用了基于排列的通道选择方法和非支配排序遗传算法II (NSGA-II)。我们使用DREAM项目的一部分公共数据集来评估我们的建议,该数据集收集了快速眼动(REM)和非REM睡眠期间的58个EEG通道,而28个受试者报告了做梦或无梦的经历。利用基于csp的特征提取与k近邻(KNN)相结合,以及通过通道选择方法识别EEG通道的多种组合,我们实现了大于0.85的有梦和无梦状态区分准确率。我们的研究结果表明,只要8-10个脑电图通道就足以进行梦境识别。在模型训练期间一次排除一个主题揭示了将模型推广到未知主题的挑战。通道选择方法已被证明可以有效地选择相关的脑电信号通道子集来对梦和无梦经历进行分类。我们的结果证明了自动梦境检测的可行性,并强调了改进机器学习泛化的必要性。
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
BioMed Research International
BioMed Research International BIOTECHNOLOGY & APPLIED MICROBIOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
6.70
自引率
0.00%
发文量
1942
审稿时长
19 weeks
期刊介绍: BioMed Research International is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies covering a wide range of subjects in life sciences and medicine. The journal is divided into 55 subject areas.
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