Validation of EEG Pre-processing Pipeline by Test-Retest Reliability

Suarez-Revelo, Jazmin Ximena; Gómez, John Fredy Ochoa; Tobón, Carlos

doi:10.1007/978-3-030-00353-1_26

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…In 2017, Shirk et al [ 39 ] tested the impact of subjective artifact removal on Event-Related Potential (ERP) results, estimating the inter-rater reliability of different subjective signal-cleaning approaches. The test–retest study by Suarez-Revelo and colleagues [ 40 , 41 ] compared different pre-processing of resting state EEG for the estimation of spectral power in six frequency bands. For specific MWL correlates estimation, a test–retest study was conducted in 2021 by Getzmann et al [ 42 ] to assess the performance of the cEEGrids recordings, which are based on C-shaped electrode arrays positioned around the ear.…”

Section: Related Workmentioning

confidence: 99%

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Mastropietro

Pirovano

Marciano

et al. 2023

Sensors

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Background and Objective: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). Methods: Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon’s task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. Results: MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). Conclusions: The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

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Section: Related Workmentioning

confidence: 99%

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Mastropietro

Pirovano

Marciano

et al. 2023

Sensors

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“…Diagram of the Pipeline for EEG Signal Pre-processing (Suárez-Revelo et al, 2018). The figure illustrates the step-by-step pipeline used for pre-processing EEG signals to enhance test-retest reliability.…”

Section: Methodsmentioning

confidence: 99%

“…Each dataset was preprocessed with a pipeline inspired by the work of Suárez-Revelo et al (2018), in order to improve test-retest reliability. The preprocessing pipeline used is explained in the following subsections and illustrated in Figure 1.…”

Section: Signal Pre-processingmentioning

confidence: 99%

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Reproducible Neuronal Components found using Group Independent Component Analysis in Resting State Electroencephalographic Data

Ochoa-Gómez,

Mantilla-Ramos,

Isaza

et al. 2023

Preprint

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ObjectiveEvaluate the reliability of neural components obtained from the appli-cation of the group ICA (gICA) methodology to resting-state EEG datasets acquired from multiple sites.MethodsFive databases from three sites, covering a total of 292 healthy subjects, were analyzed. Each dataset was segmented into groups of 15 subjects, for a total of 19 groups. Data were pre-processed using an automatic pipeline leveraging robust average referencing, wavelet-ICA and automatic rejection of epochs. On each group, stable gICA decompositions were calculated using the ICASSO methodology through a range of orders of decompositions. Each order was characterized by reliability and neuralness metrics, which were evaluated to select a single order of decomposition. Finally, using the decompositions of the selected order, a clustering analysis was performed to find the common components across the 19 groups. Each cluster was characterized by the mean scalp map, its dipole generator with its localization in Talairach coordinates, the spectral behavior of the associated time-series of the components, the assigned ICLabel class and metrics that reflected their reproducibility.ResultsLower order of decompositions benefits the gICA methodology. At this, using an order of ten, the number of reproducible components with high neuronal information tends to be around nine. Of these, the bilateral motor, frontal medial, and occipital neuronal components were the most reproducible across the different datasets, appearing in more than 89% of the 19 groups evaluated.ConclusionWe developed a workflow that allows finding reproducible spatial filters between different data sets. This contributes to the improvement of the spatial resolution of the EEG as a brain mapping technique.

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“…As such, the presence of pain may introduce fluctuations in PAF that could affect the usefulness of pre-pain classification methods. While Furman and colleagues [8] demonstrated excellent reliability of sensorimotor PAF over several days of sustained pain, an in-depth investigation of the methodological factors that may influence PAF reliability was not conducted, including the pre-processing pipeline used to reduce EEG signal noise [17, 18], the frequency window used to calculate PAF [11], the duration of the EEG recording [19], how the peak frequency is identified [5], and the sensors/region of interest chosen to calculate PAF [20]. Such an investigation would ascertain whether the reliability of PAF during pain remains similar regardless of these methodological considerations or whether reliability is compromised under certain conditions.…”

mentioning

confidence: 99%

The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability

Chowdhury

Skippen

et al. 2022

Preprint

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Many pain biomarkers fail to move from discovery to clinical application, attributed to poor reliability and feasible classifications of at-risk individuals. Preliminary evidence has shown that higher pain sensitivity is associated with slow peak alpha frequency (PAF) and depression of corticomotor excitability (CME). The present study evaluated the reliability of these measures, specifically whether, over several days of pain, a) PAF remains stable and b) individuals show two stable and distinct CME responses: facilitation and depression. Seventy-five healthy participants were given an injection of nerve growth factor (NGF) into the right masseter muscle on Day 0 and Day 2, inducing sustained pain. Electroencephalography (EEG) to assess PAF and transcranial magnetic stimulation (TMS) to assess CME were recorded on Day 0, Day 2 and Day 5. PAF reliability was in the excellent range even without standard pre-processing and ~2 minutes recording length. Moreover, two distinct and stable CME responses were demonstrated: facilitation and depression. These findings support the notion that PAF is a stable trait characteristic, with reliability unaffected by pain, and excellent reliability achievable with minimal pre-processing and ~2 minutes recording, making it a readily translatable biomarker. Furthermore, the study showed novel evidence of two stable corticomotor adaptations to sustained pain. Overall, the study provides support for the reliability of PAF and CME as prospective cortical biomarkers

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Validation of EEG Pre-processing Pipeline by Test-Retest Reliability

Cited by 7 publications

References 24 publications

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Reproducible Neuronal Components found using Group Independent Component Analysis in Resting State Electroencephalographic Data

The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability

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