Independent Component Analysis with Functional Neuroscience Data Analysis

doi:10.31661/jbpe.v0i0.2111-1436

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Data is assumed to be non-gaussian. During signal processing, ICA coefficients are computed and are used to determine that spaDal contribuDon on the signal component (Aljobouri, 2023;Carvalho, 2023). Components that appear artefactual are idenDfied and removed, and the EEG signal is then reconstructed without the artefact (Ungureanu et al, 2004).…”

Section: Independent Component Analysismentioning

confidence: 99%

Methodological approaches to derive the heartbeat evoked potential: past practices and future recommendations

Virjee,

Kandasamy,

Garfinkel

et al. 2024

Preprint

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The heartbeat-evoked potential (HEP), a cortical response time-locked to each heartbeat, is suggested as an implicit electrophysiological marker reflecting the cortical processing of heartbeats, and more broadly interoceptive processing. An increasing number of studies suggest that HEP may be a meaningful clinical measure. However, on the scalp, HEP are low amplitude signals that are mixed with direct cardiac field artefacts. Therefore, signal processing pipelines that separate the cortical HEP from cardiac field artefacts are required. With a view to establishing optimal and standardised HEP pipelines, this review aims to evaluate the current approaches to this analysis used within the literature, address gaps and inconsistencies in HEP pipelines, and highlight the impact of crucial parameter choices. To do this, a scoping review investigated current HEP processing methods and parameters used in EEG and MEG studies. Testing these processing methods on Temple University’s normal scalp EEG data (Obeid and Picone, 2016), the effect of different methods/parameters (e.g. HEP window, electrodes, filters, independent component analysis (ICA) and artefact subspace reconstruction (ASR)) on HEP extraction was explored.EEG studies (N=101) demonstrated greater parameter variability and heterogeneity than MEG studies (N=10), although significantly more studies used EEG. ICA without cardiac field artefact and ASR at threshold 20 exhibit similar results for artefact removal. Statistical analysis revealed that the RR interval, the start and end of the HEP window and the start of the baseline correction window significantly affect HEP values in pre-frontal, frontal and centrotemporal electrodes.Publications should report critical values for reliable HEP extraction, emphasising the need for standardised methods to enhance study comparison and reproducibility and establish a gold standard in the field.

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Section: Independent Component Analysismentioning

confidence: 99%

Methodological approaches to derive the heartbeat evoked potential: past practices and future recommendations

Virjee,

Kandasamy,

Garfinkel

et al. 2024

Preprint

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show abstract

“…Another method offers more effective and reliable results, although it is computationally more intensive: correction using Independent Component Analysis (ICA) [ 7 , 8 ]. ICA has become a standard tool for neuroscience data analysis [ 9 ]. It was first introduced in the 1980s as a tool for analyzing composite messages produced by a set of sensors, each sensitive to a composition of multiple source signals [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Conservative method for vertical electrooculogram attenuation based on local suppression of ongoing EEG artifact templates

Marques Abramov,

Galhanone,

Lazarev

et al. 2024

PLoS ONE

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Eye movement during blinking can be a significant artifact in Event-Related Potentials (ERP) analysis. Blinks produce a positive potential in the vertical electrooculogram (VEOG), spreading towards the posterior direction. Two methods are frequently used to suppress VEOGs: linear regression to subtract the VEOG signal from the electroencephalogram (EEG) and Independent Component Analysis (ICA). However, some information is lost in both. The present algorithm (1) statistically identifies the position of VEOGs in the frontopolar channels; (2) performs EEG averaging for each channel, which results in ’blink templates’; (3) subtracts each template from the respective EEG at each VEOG position, only when the linear correlation index between the template and the segment is greater than a chosen threshold L. The signals from twenty subjects were acquired using a behavioral test and were treated using FilterBlink for subsequent ERP analysis. A model was designed to test the method for each subject using twenty copies of the EEG signal from the subject’s mid-central channel (with nearly no VEOG) representing the EEG channels and their respective blink templates. At the same 200 equidistant time points (marks), a signal (2.5 sinusoidal cycles at 1050 ms emulating an ERP) was mixed with each model channel and the respective blink template of that channel, between 500 to 1200 ms after each mark. According to the model, VEOGs interfered with both ERPs and the ongoing EEG, mainly on the anterior medial leads, and no significant effect was observed on the mid-central channel (Cz). FilterBlink recovered approximately 90% (Fp1) to 98% (Fz) of the original ERP and EEG signals for L = 0.1. The method reduced the VEOG effect on the EEG after ERP and blink-artifact averaging in analyzing real signals. The method is straightforward and effective for VEOG attenuation without significant distortion in the EEG signal and embedded ERPs.

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Baseline correction for infrared spectra using relative absorbance-based independent component analysis

Li,

Liu,

et al. 2024

Opt. Express

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Infrared spectroscopy has important applications in fields such as materials analysis and chemical detection, while baseline correction is a key step in ensuring the accurate interpretation of spectral data. Uncorrected baselines can lead to deviations in absorption peaks, which affects the accuracy of both quantitative and qualitative analysis. When the absorption peaks of various components in a mixed gas overlap, the lack of reference baseline information in the continuous absorption band can result in significant errors during the baseline correction process. In this paper, we propose a relative absorbance-based independent component analysis (RA-ICA) algorithm to address this challenge. The algorithm first calculates the relative absorbance spectrum that excludes baseline information. Subsequently, based on the Beer-Lambert law and independent component analysis, it extracts independent components containing the absorption peak information of the components, allowing for fitting spectra that require baseline correction. Finally, a baseline model that combines polynomial curves and residuals is used to reconstruct the hidden baseline of the absorption band. Simulation and experimental results demonstrate that the baseline reconstructed using the RA-ICA method exhibits a significantly lower error compared to five other commonly used baseline correction methods and accurately preserves the detailed characteristics of the baseline.

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Independent Component Analysis with Functional Neuroscience Data Analysis

Cited by 4 publications

References 31 publications

Methodological approaches to derive the heartbeat evoked potential: past practices and future recommendations

Methodological approaches to derive the heartbeat evoked potential: past practices and future recommendations

Conservative method for vertical electrooculogram attenuation based on local suppression of ongoing EEG artifact templates

Baseline correction for infrared spectra using relative absorbance-based independent component analysis

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