Toward Improving fNIRS Classification: A Study on Activation Functions in Deep Neural Architectures

TL;DR

This study systematically evaluates various activation functions in deep neural networks for fNIRS classification, revealing that symmetrical functions like Tanh and Abs(x) can outperform ReLU, thereby improving model accuracy in noisy, low-SNR conditions.

Contribution

It provides the first comprehensive analysis of activation functions tailored for fNIRS deep learning models, highlighting the benefits of symmetry in activation functions for this domain.

Findings

Symmetrical activation functions like Tanh outperform ReLU in fNIRS classification.

Absolute value activation functions can lead to better performance depending on architecture.

Symmetry in activation functions enhances deep learning performance on noisy fNIRS data.

Abstract

Activation functions are critical to the performance of deep neural networks, particularly in domains such as functional near-infrared spectroscopy (fNIRS), where nonlinearity, low signal-to-noise ratio (SNR), and signal variability poses significant challenges to model accuracy. However, the impact of activation functions on deep learning (DL) performance in the fNIRS domain remains underexplored and lacks systematic investigation in the current literature. This study evaluates a range of conventional and field-specific activation functions for fNIRS classification tasks using multiple deep learning architectures, including the domain-specific fNIRSNet, AbsoluteNet, MDNN, and shallowConvNet (as the baseline), all tested on a single dataset recorded during an auditory task. To ensure fair a comparison, all networks were trained and tested using standardized preprocessing and consistent training parameters. The results show that symmetrical activation functions such as Tanh and the Absolute value function Abs(x) can outperform commonly used functions like the Rectified Linear Unit (ReLU), depending on the architecture. Additionally, a focused analysis of the role of symmetry was conducted using a Modified Absolute Function (MAF), with results further supporting the effectiveness of symmetrical activation functions on performance gains. These findings underscore the importance of selecting proper activation functions that align with the signal characteristics of fNIRS data.