Abstract
Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control.
Highlights
Functional near-infrared spectroscopy is a non-invasive neuroimaging modality which allows the detection of cortical brain activity through the use of light in the near-infrared spectrum (650–900 nm)
A copy of the segments used in this study, labeled dataset, and pre-trained network are available online on the data repository of this project [38], while the template for the web platform fNIRSQC has been released as an open sourced project under the name cisciqc (Citizen Science Quality Control [39])
In this work, we have demonstrated a proof-of-concepts of how a Deep Neural Network (DNN) model can be trained and employed to classify the usability of Functional near-infrared spectroscopy (fNIRS) signals
Summary
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality which allows the detection of cortical brain activity through the use of light in the near-infrared spectrum (650–900 nm). Despite the burgeoning use of fNIRS, a general consensus or standardization of the best pre-processing practices for the NIRS signal has not been established, unlike other neuroimaging modalities such as functional magnetic resonance imaging (fMRI; see [4,5]). Differences in the use and combination of pre-processing pipelines have been demonstrated to lead to different results in fNIRS studies [6]. The absence of standardization in pre-processing methods, analysis tools, and instrumentation can lead to the scarce reproducibility of studies and results, similar to what occurs with other neurophysiological signals (e.g., infant cry [7])
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