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

• URL: http://arxiv.org/abs/2506.00039v1
• Date: Tue, 27 May 2025 19:21:17 GMT
• Title: AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing
• Authors: Behtom Adeli, John Mclinden, Pankaj Pandey, Ming Shao, Yalda Shahriari,
• Abstract summary: This work introduces AbsoluteNet, a novel deep learning architecture designed to classify auditory event-related responses using fNIRS.<n>The network is built upon principles of convolution and customized activation functions.<n>Results showed that AbsoluteNet outperforms existing models, reaching 87.0% accuracy, 84.8% sensitivity, and 89.2% specificity in binary classification.
• Score: 7.243563999211656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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