Core Concepts
Integration of calibration is crucial for enhancing the reliability of deep learning-based predictions in fNIRS classification tasks.
Abstract
The content discusses the importance of calibration in functional near-infrared spectroscopy (fNIRS) classification models. It highlights the significance of reliability and proposes practical tips to improve calibration performance. The article emphasizes the critical role of calibration in fNIRS research and argues for enhancing the reliability of deep learning-based predictions. Various metrics and techniques are explored to evaluate model calibration, including Expected Calibration Error (ECE), Maximum Calibration Error (MCE), Overconfidence Error (OE), Static Calibration Error (SCE), Adaptive Calibration Error (ACE), and Temperature Scaling. Experimental results on different datasets demonstrate the impact of calibration on model performance, accuracy, and reliability.
I. INTRODUCTION
fNIRS as a non-invasive tool for monitoring brain activity.
Importance of understanding fNIRS signals for brain-computer interfaces.
II. FUNCTIONAL NEAR-INFRARED SPECTROSCOPY DATASET
Utilization of open-source datasets for experiments.
III. CALIBRATION ERROR
Explanation of various metrics like ECE, MCE, OE, SCE, ACE, TACE.
IV. EXPERIMENT
Signal preprocessing methods for MA and UFFT datasets.
Training settings and evaluation processes for deep learning models.
V. PRACTICAL SKILLS
Balancing accuracy and calibration using evaluation metrics.
Model capacity selection impact on calibration performance.
Temperature scaling technique to reduce calibration error.
VI. CONCLUSION
Proposal to integrate calibration into fNIRS field for enhancing model reliability.
Stats
"Avg. Acc: 0.73, Avg. Conf: 0.82"
"Avg. Acc: 0.72, Avg. Conf: 0.78"