Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104.
Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
eNeuro. 2023 Sep 5;10(9). doi: 10.1523/ENEURO.0050-23.2023. Print 2023 Sep.
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, 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.
脑电相位在认知神经科学、脑机接口和闭环刺激设备中被越来越多地应用。然而，目前尚不清楚在认知状态下脑电相位的预测精度如何。我们在 11 个公共数据集上，对 484 名参与者在休息和任务状态下的顶枕部 alpha 波脑电相位预测精度进行了研究。我们能够在各种认知条件和数据集之间准确地跟踪脑电相位，尤其是在瞬时 alpha 功率和信噪比 (SNR) 较高的时期。虽然休息状态通常比任务状态具有更高的准确性，但绝对准确性差异很小，这些差异主要归因于脑电功率和 SNR。这些结果表明，使用脑电相位的实验和技术应该更加注重最小化外部噪声和等待高功率期，而不是诱导特定的认知状态。
