用深度学习提升脑电活动分类准确率，助力听觉信息解码
AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing
- 基于时空卷积与定制激活函数设计新网络结构
- 在二分类任务中达87.0%准确率，优于现有模型3.8个百分点
- 适合关注脑机接口与近红外脑成像分析的研究者
近年来，深度学习方法在解码功能近红外光谱（fNIRS）记录的血流动力学响应方面展现出良好前景，尤其在脑机接口（BCI）应用中。本文提出AbsoluteNet，一种新型深度学习架构，用于分类通过fNIRS记录的听觉事件相关反应。该网络基于时空卷积与定制激活函数设计。与fNIRSNET、MDNN、DeepConvNet和ShallowConvNet等模型相比，AbsoluteNet在二分类任务中达到87.0%的准确率、84.8%的敏感性与89.2%的特异性，较表现最好的对照模型fNIRSNET高出3.8%。结果表明，所提模型在解码听觉处理相关的血流动力学响应方面具有显著优势，凸显了时空特征聚合与定制激活函数对适配fNIRS动态特性的重要性。
原文摘要 · Abstract (English)
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.
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