Journal article
A - 175 EEG Phase Can be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and SNR
Archives of clinical neuropsychology, v 38(7), pp 1347-1348
20 Oct 2023
PMID: 37807372
Abstract
Abstract
Objective
Electroencephalogram (EEG) phase can capture neural oscillation dynamics. Accordingly, EEG phase is a potential target for neurofeedback and brain-computer interfaces (BCIs). However, rigorous tests of the generalizability of real-time EEG phase are needed, requiring accurate phase estimates. We examined how cognitive states affect EEG phase prediction accuracy in the parieto-occipital alpha band.
Data Selection
We identified datasets through public repositories. After preprocessing, we used the Educated Temporal Prediction Algorithm (ETP) to predict future peaks. We compared these predictions to the original signal, and defined accuracy from 0 to 100%. Our independent variable was cognitive state (eyes-open rest, eyes-closed rest, task), dependent variable was accuracy, and covariates were EEG instantaneous power and signal-to-noise ratio (SNR). We used a linear mixed-effects model, with random effects for dataset and individual.
Data Synthesis
Across the 11 datasets, we had 543 participants and 1,641,074 predictions. There were no significant accuracy differences among the conditions (p < 1e-5), with a baseline accuracy of 59.07%. Power had an effect on accuracy, with a unit increase leading to a 13.1% increase in accuracy (p < 1e-5). SNR had an effect on accuracy, with an effect size of 0.46% (p < 1e-5), with a significant negative interaction with power with an effect size of −0.51% (p < 1e-5).
Conclusion
Our results indicated that we could predict EEG phase accurately across cognitive conditions and datasets, with higher accuracy for high instantaneous band-power and SNR. Accordingly, real-time EEG phase experiments, closed-loop technologies, and BCIs should minimize external unwanted noise while targeting periods of high power, as opposed to manipulating experimental and cognitive conditions.
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Details
- Title
- A - 175 EEG Phase Can be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and SNR
- Creators
- Brian KimBrian A Erickson - Drexel University, Psychological and Brain Sciences (Psychology)Guadalupe Fernandez-Nuñez - Drexel University, Psychological and Brain Sciences (Psychology)John D Medaglia - Drexel University, Psychological and Brain Sciences (Psychology)Georgios Mentzelopoulos - University of PennsylvaniaRyan R Rich - Drexel University, Psychological and Brain Sciences (Psychology)Flavia Vitale - University of Pennsylvania
- Publication Details
- Archives of clinical neuropsychology, v 38(7), pp 1347-1348
- Publisher
- Oxford University Press
- Number of pages
- 2
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:001082856000001
- Scopus ID
- 2-s2.0-85175269835
- Other Identifier
- 991021416635304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Psychology
- Psychology, Clinical