Yu Kaiwei, Chen Jiafa, Ding Xian, Zhang Dawei
Research Center of Optical Instrument and System, Ministry of Education and Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai, 200093, China.
Med Biol Eng Comput. 2025 Jan;63(1):45-57. doi: 10.1007/s11517-024-03178-w. Epub 2024 Aug 6.
Cognition is crucial to brain function, and accurately classifying cognitive load is essential for understanding the psychological processes across tasks. This paper innovatively combines functional near-infrared spectroscopy (fNIRS) with eye tracking technology to delve into the classification of cognitive load at the neurocognitive level. This integration overcomes the limitations of a single modality, addressing challenges such as feature selection, high dimensionality, and insufficient sample capacity. We employ fNIRS-eye tracking technology to collect neural activity and eye tracking data during various cognitive tasks, followed by preprocessing. Using the maximum relevance minimum redundancy algorithm, we extract the most relevant features and evaluate their impact on the classification task. We evaluate the classification performance by building models (naive Bayes, support vector machine, K-nearest neighbors, and random forest) and employing cross-validation. The results demonstrate the effectiveness of fNIRS-eye tracking, the maximum relevance minimum redundancy algorithm, and machine learning techniques in discriminating cognitive load levels. This study emphasizes the impact of the number of features on performance, highlighting the need for an optimal feature set to improve accuracy. These findings advance our understanding of neuroscientific features related to cognitive load, propelling neural psychology research to deeper levels and holding significant implications for future cognitive science.
认知对大脑功能至关重要，准确分类认知负荷对于理解各项任务中的心理过程至关重要。本文创新性地将功能性近红外光谱技术（fNIRS）与眼动追踪技术相结合，深入探讨神经认知水平上的认知负荷分类。这种整合克服了单一模态的局限性，解决了特征选择、高维度和样本容量不足等挑战。我们采用fNIRS-眼动追踪技术在各种认知任务期间收集神经活动和眼动追踪数据，随后进行预处理。使用最大相关最小冗余算法，我们提取最相关的特征并评估它们对分类任务的影响。我们通过构建模型（朴素贝叶斯、支持向量机、K近邻和随机森林）并采用交叉验证来评估分类性能。结果证明了fNIRS-眼动追踪技术、最大相关最小冗余算法和机器学习技术在区分认知负荷水平方面的有效性。本研究强调了特征数量对性能的影响，突出了需要一个最优特征集来提高准确性。这些发现推进了我们对与认知负荷相关的神经科学特征的理解，将神经心理学研究推向更深层次，对未来认知科学具有重要意义。
