Jin Kaizhe, Rubio-Solis Adrian, Naik Ravi, Leff Daniel, Kinross James, Mylonas George
Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London SW7 2AZ, UK.
Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK.
Sensors (Basel). 2025 Jul 5;25(13):4207. doi: 10.3390/s25134207.
This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplore, and PubMed. Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. The review highlights a pivotal shift from shallow ML to DL approaches for analysing physiological signals, driven by DL's ability to autonomously learn complex patterns in large datasets. By 2023, DL has become the dominant methodology, though traditional ML techniques remain relevant. Additionally, there has been a move from neuroimaging to multimodal physiological modalities, with the decrease in neuroimaging use reflecting a trend towards integrating various physiological signals for more comprehensive insights. Cognitive state recognition is applied across diverse domains such as the automotive, aviation, maritime, and healthcare industries, enhancing performance and safety in high-stakes environments. Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. The trend in cognitive state recognition research is moving towards integrating ECG signals with CNNs and adopting privacy-preserving methodologies like differential privacy and federated learning, highlighting the potential of cognitive state recognition to enhance performance, safety, and innovation across various real-world applications.
本系统综述分析了2010年至2024年初认知状态识别方面的进展，从通过Scopus、工程村、科学网、IEEE Xplore和PubMed这五个数据库识别出的2398条记录的初始库中评估了405篇相关文章。如果研究使用生理信号评估认知状态，并在实际任务设置中应用机器学习（ML）或深度学习（DL）技术，则纳入该综述。该综述强调了从浅层机器学习向深度学习方法分析生理信号的关键转变，这是由深度学习在大型数据集中自主学习复杂模式的能力驱动的。到2023年，深度学习已成为主导方法，尽管传统机器学习技术仍然相关。此外，出现了从神经成像向多模态生理模式的转变，神经成像使用的减少反映了一种趋势，即整合各种生理信号以获得更全面的见解。认知状态识别应用于汽车、航空、航海和医疗保健等多个领域，在高风险环境中提高了性能和安全性。心电图（ECG）是使用最多的模式，卷积神经网络（CNN）是主要的深度学习方法。认知状态识别研究的趋势正朝着将心电图信号与卷积神经网络相结合，并采用差分隐私和联邦学习等隐私保护方法发展，突出了认知状态识别在增强各种实际应用中的性能、安全性和创新性方面的潜力。
