# Simple But Effective: Rethinking the Ability of Deep Learning in fNIRS to Exclude Abnormal Input

arXiv: https://arxiv.org/abs/2402.18112 · HTML: https://mlsanity.com/paper/2402.18112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)
- [Metric Learning for Novelty and Anomaly Detection](https://mlsanity.com/paper/1808.05492/) (2018)
- [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)
- [Calibration of Deep Learning Classification Models in fNIRS](https://mlsanity.com/paper/2402.15266/) (2024)

## Abstract (arXiv)

Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique for monitoring brain activity. To better understand the brain, researchers often use deep learning to address the classification challenges of fNIRS data. Our study shows that while current networks in fNIRS are highly accurate for predictions within their training distribution, they falter at identifying and excluding abnormal data which is out-of-distribution, affecting their reliability. We propose integrating metric learning and supervised methods into fNIRS research to improve networks capability in identifying and excluding out-of-distribution outliers. This method is simple yet effective. In our experiments, it significantly enhances the performance of various networks in fNIRS, particularly transformer-based one, which shows the great improvement in reliability. We will make our experiment data available on GitHub.