Dataset Information

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.

ABSTRACT: Researchers using functional near infrared spectroscopy (fNIRS) are increasingly aware of the problem that conventional filtering methods do not eliminate systemic noise at frequencies overlapping with the task frequency. This is a problem when signals are averaged for analysis, even more so when single trial data are used as in online neurofeedback or BCI applications where insufficiently preprocessed data means feeding back noise instead of brain activity or when looking for brain-behavior relationships on a trial-by-trial basis. For removing this task-related noise statistical approaches have been proposed. Yet as evidence is lacking on how these approaches perform on independent data, choosing one approach over another can be difficult. Here signal quality at the single trial level was considered together with statistical effects to inform this choice. Compared were conventional band-pass filtering and wavelet minimum description length detrending and the combination of both with a more elaborate, published preprocessing approach for a motor execution-motor imagery data set. Temporal consistency between ?[HbO] and ?[HbR] and two measures of the spatial specificity of signals that are proposed here served as measures of data quality. Both improved strongly for the combinationed preprocessing approaches. Statistical effects showed a strong tendency toward getting smaller for the combined approaches. This underlines the importance to adequately deal with noise in fNIRS recordings and demonstrates how the quality of statistical correction approaches can be estimated.

SUBMITTER: Klein F

PROVIDER: S-EPMC6769087 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.

Klein Franziska F Kranczioch Cornelia C

Frontiers in human neuroscience 20190924

Researchers using functional near infrared spectroscopy (fNIRS) are increasingly aware of the problem that conventional filtering methods do not eliminate systemic noise at frequencies overlapping with the task frequency. This is a problem when signals are averaged for analysis, even more so when single trial data are used as in online neurofeedback or BCI applications where insufficiently preprocessed data means feeding back noise instead of brain activity or when looking for brain-behavior rel ...[more]

PMID: 31607880

Similar Datasets

Project description:Evidence using well-established imaging techniques, such as functional magnetic resonance imaging and electrocorticography, suggest that speech-specific cortical responses can be functionally localised by contrasting speech responses with an auditory baseline stimulus, such as time-reversed (TR) speech or signal-correlated noise (SCN). Furthermore, these studies suggest that SCN is a more effective baseline than TR speech. Functional near-infrared spectroscopy (fNIRS) is a relatively novel, optically-based imaging technique with features that make it ideal for investigating speech and language function in paediatric populations. However, it is not known which baseline is best at isolating speech activation when imaging using fNIRS. We presented normal speech, TR speech and SCN in an event-related format to 25 normally-hearing children aged 6-12 years. Brain activity was measured across frontal and temporal brain areas in both cerebral hemispheres whilst children passively listened to the auditory stimuli. In all three conditions, significant activation was observed bilaterally in channels targeting superior temporal regions when stimuli were contrasted against silence. Unlike previous findings in infants, we found no significant activation in the region of interest over superior temporal cortex in school-age children when normal speech was contrasted against either TR speech or SCN. Although no statistically significant lateralisation effects were observed in the region of interest, a left-sided channel targeting posterior temporal regions showed significant activity in response to normal speech only, and was investigated further. Significantly greater activation was observed in this left posterior channel compared to the corresponding channel on the right side under the normal speech vs SCN contrast only. Our findings suggest that neither TR speech nor SCN are suitable auditory baselines for functionally isolating speech-specific processing in an experimental set up involving fNIRS with 6-12 year old children.

Project description:The functional near-infrared spectroscopy (fNIRS) can detect hemodynamic responses in the brain and the data consist of bivariate time series of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) on each channel. In this study, we investigate oscillatory changes in infant fNIRS signals by using the oscillator decompisition method (OSC-DECOMP), which is a statistical method for extracting oscillators from time series data based on Gaussian linear state space models. OSC-DECOMP provides a natural decomposition of fNIRS data into oscillation components in a data-driven manner and does not require the arbitrary selection of band-pass filters. We analyzed 18-ch fNIRS data (3 minutes) acquired from 21 sleeping 3-month-old infants. Five to seven oscillators were extracted on most channels, and their frequency distribution had three peaks in the vicinity of 0.01-0.1 Hz, 1.6-2.4 Hz and 3.6-4.4 Hz. The first peak was considered to reflect hemodynamic changes in response to the brain activity, and the phase difference between oxy-Hb and deoxy-Hb for the associated oscillators was at approximately 230 degrees. The second peak was attributed to cardiac pulse waves and mirroring noise. Although these oscillators have close frequencies, OSC-DECOMP can separate them through estimating their different projection patterns on oxy-Hb and deoxy-Hb. The third peak was regarded as the harmonic of the second peak. By comparing the Akaike Information Criterion (AIC) of two state space models, we determined that the time series of oxy-Hb and deoxy-Hb on each channel originate from common oscillatory activity. We also utilized the result of OSC-DECOMP to investigate the frequency-specific functional connectivity. Whereas the brain oscillator exhibited functional connectivity, the pulse waves and mirroring noise oscillators showed spatially homogeneous and independent changes. OSC-DECOMP is a promising tool for data-driven extraction of oscillation components from biological time series data.

Dataset Information

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.

Publications

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets