Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification
Unsupervised fNIRS feature extraction with CAE and ESN autoencoder for driver cognitive load classification
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DOI：
10.1088/1741-2552/abd2ca
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发表时间：
2020-12
影响因子：
4
通讯作者：
Ruixue Liu;B. Reimer;Siyang Song;Bruce Mehler;E. Solovey
中科院分区：
文献类型：
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作者：
Ruixue Liu;B. Reimer;Siyang Song;Bruce Mehler;E. Solovey
目标。了解驾驶员的认知负荷对道路安全至关重要。大脑感知有可能提供驾驶员认知负荷的客观测量。我们的目标是开发一个先进的机器学习框架，用于使用功能近红外光谱（fNIRS）对驾驶员认知负荷进行分类。的方法。我们在驾驶模拟器中使用fNIRS进行了一项研究，并将N-back任务作为次要任务，向驾驶员施加结构化认知负荷。为了对不同驾驶员认知负荷水平进行分类，研究了卷积自编码器（CAE）和回声状态网络（ESN）自编码器在近红外光谱特征提取中的应用。主要的结果。采用CAE方法，以30秒窗对二级和四级驾驶员认知负荷进行分类的准确率分别为73.25%和47.21%。所提出的ESN自编码器在没有窗口选择的情况下，对组级模型进行分类，准确率分别为80.61%和52.45%。的意义。这项工作为在实际应用中使用近红外光谱测量驾驶员认知负荷奠定了基础。结果表明，所提出的回声状态网络自编码器可以有效地从fNIRS数据中提取时间信息，并可用于其他fNIRS数据分类任务。
Objective. Understanding the cognitive load of drivers is crucial for road safety. Brain sensing has the potential to provide an objective measure of driver cognitive load. We aim to develop an advanced machine learning framework for classifying driver cognitive load using functional near-infrared spectroscopy (fNIRS). Approach. We conducted a study using fNIRS in a driving simulator with the N-back task used as a secondary task to impart structured cognitive load on drivers. To classify different driver cognitive load levels, we examined the application of convolutional autoencoder (CAE) and Echo State Network (ESN) autoencoder for extracting features from fNIRS. Main results. By using CAE, the accuracies for classifying two and four levels of driver cognitive load with the 30 s window were 73.25% and 47.21%, respectively. The proposed ESN autoencoder achieved state-of-art classification results for group-level models without window selection, with accuracies of 80.61% and 52.45% for classifying two and four levels of driver cognitive load. Significance. This work builds a foundation for using fNIRS to measure driver cognitive load in real-world applications. Also, the results suggest that the proposed ESN autoencoder can effectively extract temporal information from fNIRS data and can be useful for other fNIRS data classification tasks.