Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach

Kaur, Rachneet; Korolkov, Maxim; Hernandez, Manuel E.; Sowers, Richard B.

doi:10.1109/embc44109.2020.9175741

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different studies show that preprocessing EEG signals has a big impact on the final results (Vajravelu et al, 2021;Pedroni et al, 2019). Therefore, it is necessary to find ways to identify and separate the different sources of noise in order to obtain a clean EEG signal for subsequent analyses (Jiang et al, 2019;Kaur et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

et al. 2023

View full text Add to dashboard Cite

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built.

show abstract