基于脑电信号的认知与静息状态深度学习分类
Deep Learning-Based Classification of Cognitive and Resting States Using Electroencephalography Signals
- RMIT University（皇家墨尔本理工大学）
- Institute of Engineering & Management, Kolkata（加尔各答工程与管理学院）
- University of Engineering and Management, Kolkata（加尔各答工程与管理大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出一种结合CNN和GRU的深度学习框架，利用EEG信号时频特征区分静息与认知状态，在数学、记忆和音乐任务中分别达到83.177%、76.107%和83.432%的准确率。
AI中文摘要：
基于脑电图（EEG）信号对认知状态和静息状态进行分类，对于理解与不同心理状态相关的脑活动波动至关重要。EEG提供了一种非侵入性的方法来记录静息状态和任务导向的认知条件下的脑功能，而深度学习技术能够从复杂的EEG数据中自动提取重要模式。本研究提出了一个深度学习框架，通过EEG记录来区分静息状态和认知状态。该框架整合了卷积神经网络（CNN）和门控循环单元（GRU）堆叠的结构，用于从EEG信号中提取特征。进行了时频分析以探索信号的显著方面，然后利用传统的深度学习和机器学习分类器（包括所提出的2D-Net架构）评估所提取的特征。所提出的方法和特征提取策略优于评估的比较方法，在静息状态与数学任务分类中达到了83.177%的准确率，在静息状态与记忆任务分类中达到了76.107%的准确率，在静息状态与音乐任务分类中达到了83.432%的准确率。研究结果证明了将信号处理与深度学习方法相结合以利用EEG信号区分静息状态与认知状态的有效性。
英文摘要：
The categorization of cognitive and resting states derived from electroencephalography (EEG) signals is crucial for comprehending fluctuations in brain activity linked to various mental states. EEG provides a non-intrusive approach for documenting brain function in both resting and task-oriented cognitive conditions, whilst deep learning techniques enable the automatic extraction of significant patterns from intricate EEG data. This study presents a deep learning framework to distinguish between resting and cognitive states through EEG records. The proposed framework integrates a Convolutional Neural Network (CNN) stacked with a Gated Recurrent Unit (GRU) for the extraction of features from EEG signals. Time-frequency analysis is conducted to explore the salient aspects of signals, and the derived features are then assessed utilizing conventional deep learning and machine learning classifiers, including the suggested 2D-Net architecture. The proposed approach and feature extraction strategy outperform the evaluated comparative methods, achieving accuracies of 83.177% for resting-versus-mathematical task classification, 76.107% for resting-versus-memory task classification, and 83.432% for resting-versus-music task classification. The findings illustrate the efficacy of integrating signal processing with deep learning methodologies to discriminate resting from cognitive states utilizing EEG signals.
