Christian Gerloff, Meryem Yücel, Lena Mehlem, Kerstin Konrad, Vanessa Reindl
Neurophotonics, Vol. 11, Issue 04, 045008, (November 2024) https://doi.org/10.1117/1.NPh.11.4.045008
TOPICS: Sensors, Machine learning, Signal detection, Contamination, Education and training, Neurophotonics, Signal attenuation, Near infrared spectroscopy, Signal to noise ratio, Interference (communication)
Significance
The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these “bad channels,” little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored.
Aim
We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches.
Approach
We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simulated scenario space of diverse phenomena, we evaluated the NiReject models, six of the most established detection methods in fNIRS, and 11 prominent methods from other domains.
Results
Although the results indicated that a lack of proper detection can strongly bias findings, detection methods were reported in only 32% of the included studies. Semi-supervised models, specifically semi-supervised NiReject, outperformed both established thresholding-based and unsupervised detectors. Hybrid NiReject, utilizing a human feedback loop, addressed the practical challenges of semi-supervised methods while maintaining precise detection and low rating effort.
Conclusions
This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.