标题： 绝对网：用于分类听觉处理的大脑灌注反应的深度学习神经网络 显示英文标题

标题： AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing

摘要： 近年来，深度学习（DL）方法在解码由功能性近红外光谱（fNIRS）捕获的血流动力学反应方面展示了有前景的结果，尤其是在脑机接口（BCI）应用的背景下。 本研究介绍了一种名为AbsoluteNet的新深度学习架构，该架构旨在对使用fNIRS记录的听觉事件相关反应进行分类。 所提出的网络基于时空卷积和自定义激活函数的设计原则构建。 我们的模型与几种模型进行了比较，分别是fNIRSNET、MDNN、DeepConvNet和ShallowConvNet。 结果显示，AbsoluteNet在二分类任务中的准确率达到87.0%，敏感性达到84.8%，特异性达到89.2%，比第二好的模型fNIRSNET在准确率上高出3.8%。 这些发现强调了我们提出的深度学习模型在解码与听觉处理相关的血流动力学反应方面的有效性，并突显了时空特征聚合和自定义激活函数对于更好地适应fNIRS动态的重要性。 显示英文摘要

摘要： In recent years, deep learning (DL) approaches have demonstrated promising results in decoding hemodynamic responses captured by functional near-infrared spectroscopy (fNIRS), particularly in the context of brain-computer interface (BCI) applications. This work introduces AbsoluteNet, a novel deep learning architecture designed to classify auditory event-related responses recorded using fNIRS. The proposed network is built upon principles of spatio-temporal convolution and customized activation functions. Our model was compared against several models, namely fNIRSNET, MDNN, DeepConvNet, and ShallowConvNet. The results showed that AbsoluteNet outperforms existing models, reaching 87.0% accuracy, 84.8% sensitivity, and 89.2% specificity in binary classification, surpassing fNIRSNET, the second-best model, by 3.8% in accuracy. These findings underscore the effectiveness of our proposed deep learning model in decoding hemodynamic responses related to auditory processing and highlight the importance of spatio-temporal feature aggregation and customized activation functions to better fit fNIRS dynamics.