Carlos Guerrero-Mosquera1, Guillermo Borragán2, Philippe Peigneux3. 1. Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), ULB Neuroscience Institute (UNI), Bruxelles, Belgium. Electronic address: carlos.guerrero@upf.edu. 2. Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), ULB Neuroscience Institute (UNI), Bruxelles, Belgium. Electronic address: gborraga@ulb.be. 3. Neuropsychology and Functional Neuroimaging Research Unit (UR2NF), ULB Neuroscience Institute (UNI), Bruxelles, Belgium.
Abstract
BACKGROUND: fNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels. METHODS: In the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers. RESULTS: The validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm. COMPARISON WITH EXISTING METHOD(S): Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations). CONCLUSIONS: Our results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost.
BACKGROUND: fNIRS signals can be contaminated by distinct sources of noise. While most of the noise can be corrected using digital filters, optimized experimental paradigms or pre-processing methods, few approaches focus on the automatic detection of noisy channels. METHODS: In the present study, we propose a new method that detect automatically noisy fNIRS channels by combining the global correlations of the signal obtained from sliding windows (Cui et al., 2010) with correlation coefficients extracted experimental conditions defined by triggers. RESULTS: The validity of the method was evaluated on test data from 17 participants, for a total of 16 NIRS channels per subject, positioned over frontal, dorsolateral prefrontal, parietal and occipital areas. Additionally, the detection of noisy channels was tested in the context of different levels of cognitive requirement in a working memory N-back paradigm. COMPARISON WITH EXISTING METHOD(S): Bad channels detection accuracy, defined as the proportion of bad NIRS channels correctly detected among the total number of channels examined, was close to 91%. Under different cognitive conditions the area under the Receiver Operating Curve (AUC) increased from 60.5% (global correlations) to 91.2% (local correlations). CONCLUSIONS: Our results show that global correlations are insufficient for detecting potentially noisy channels when the whole data signal is included in the analysis. In contrast, adding specific local information inherent to the experimental paradigm (e.g., cognitive conditions in a block or event-related design), improved detection performance for noisy channels. Also, we show that automated fNIRS channel detection can be achieved with high accuracy at low computational cost.
Authors: Guillermo Borragán; Médhi Gilson; Anne Atas; Hichem Slama; Andreas Lysandropoulos; Melanie De Schepper; Philippe Peigneux Journal: Front Hum Neurosci Date: 2018-09-20 Impact factor: 3.169