# Calibration of Deep Learning Classification Models in fNIRS

arXiv: https://arxiv.org/abs/2402.15266 · HTML: https://mlsanity.com/paper/2402.15266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks](https://mlsanity.com/paper/1905.11001/) (2019)
- [Improving model calibration with accuracy versus uncertainty optimization](https://mlsanity.com/paper/2012.07923/) (2020)
- [Deep Learning in fNIRS: A review](https://mlsanity.com/paper/2201.13371/) (2022)
- [Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS](https://mlsanity.com/paper/2007.06706/) (2020)
- [A novel multimodal approach for hybrid brain-computer interface](https://mlsanity.com/paper/2004.12081/) (2020)

## Built on by (most cited first, 1 of 1)

- [Simple But Effective: Rethinking the Ability of Deep Learning in fNIRS to Exclude Abnormal Input](https://mlsanity.com/paper/2402.18112/) (2024)

## Abstract (arXiv)

Functional near-infrared spectroscopy (fNIRS) is a valuable non-invasive tool for monitoring brain activity. The classification of fNIRS data in relation to conscious activity holds significance for advancing our understanding of the brain and facilitating the development of brain-computer interfaces (BCI). Many researchers have turned to deep learning to tackle the classification challenges inherent in fNIRS data due to its strong generalization and robustness. In the application of fNIRS, reliability is really important, and one mathematical formulation of the reliability of confidence is calibration. However, many researchers overlook the important issue of calibration. To address this gap, we propose integrating calibration into fNIRS field and assess the reliability of existing models. Surprisingly, our results indicate poor calibration performance in many proposed models. To advance calibration development in the fNIRS field, we summarize three practical tips. Through this letter, we hope to emphasize the critical role of calibration in fNIRS research and argue for enhancing the reliability of deep learning-based predictions in fNIRS classification tasks. All data from our experimental process are openly available on GitHub.