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

arXiv: https://arxiv.org/abs/2506.00039 · HTML: https://mlsanity.com/paper/2506.00039/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 2 of 2)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Deep Learning in fNIRS: A review](https://mlsanity.com/paper/2201.13371/) (2022)

## Built on by (most cited first, 2 of 2)

- [Toward Improving fNIRS Classification: A Study on Activation Functions in Deep Neural Architectures](https://mlsanity.com/paper/2507.11436/) (2025)
- [Region-Aware Reconstruction Strategy for Pre-training fMRI Foundation Model](https://mlsanity.com/paper/2511.00443/) (2025)

## Abstract (arXiv)

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.