Logo image
Back
EEG Phase Can Be Predicted With Similar Accuracy Across Cognitive States After Accounting For Power and SNR
Journal article   Open access   Peer reviewed

EEG Phase Can Be Predicted With Similar Accuracy Across Cognitive States After Accounting For Power and SNR

B Kim, B A Erickson, G Fernandez-Nunez, R Rich, G Mentzelopoulos, F Vitale and J Medaglia
eNeuro, pENEURO.0050-23.2023
07 Aug 2023
DOI: https://doi.org/10.1523/ENEURO.0050-23.2023
PMID: 37558464
url
https://doi.org/10.1523/ENEURO.0050-23.2023View
Published, Version of Record (VoR)CC BY-NC-SA V4.0,  Open

Abstract

Phase Brain-computer Interfaces Neural Oscillations EEG Power losed-loop
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces (BCIs) and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power, rather than inducing a particular cognitive state. EEG phase is a neural signal related to many moment-to-moment behaviors and has consequently been used to inform brain-computer interfaces and closed-loop stimulation devices. However, prior research and demonstrations have forced the user to be in a single cognitive state, such as rest, making it unclear how EEG phase can apply to the varied contexts that real individuals are placed under. The current study showed that EEG phase can be consistently well predicted across different cognitive contexts after accounting for EEG power and signal-to-noise ratio. These findings represent an important next step for both understanding the cognitive and neurobiological correlates of EEG phase and optimizing EEG-based devices to administer more effective interventions.

Metrics

15 Record Views
3 citations in Scopus

Details

Logo image