The Effect of Rest and Task States on EEG Phase Prediction Accuracy
Authors:   , Kim Brian , Fernandez-Nunez Guadalupe , Erickson Brian Albert , Medaglia John D. 
Published: August 2022

Single-trial analyses of EEG signals have demonstrated a relationship between the instantaneous phase and cortical excitability (Bergmann et al., 2012; Massimini et al., 2003; Thies et al., 2018), sensory thresholding (Ai & Ro, 2014), and intracranial electrical activity (Haegens et al., 2011; Miller et al., 2012). Considering the relationships associated with EEG phase, accurately predicting phase in real-time has multiple benefits. The ability to accurately target phase will give researchers greater power in future phase-behavior experiments, potentially revealing how phasic processes are implemented and maintained in neuronal populations. Accurate phase prediction can help neuromodulation techniques in a closed-loop fashion, particularly non-invasive brain stimulation (Zrenner et al., 2018), which may operate through phase-resetting mechanisms (Kawasaki et al., 2014). Brain-computer interfaces implementing phase measures (Brunner et al., 2006; Hsu, 2015) will also benefit from increased real-time phase estimation. The current cognitive faculty (e.g., attention) an individual is engaged in affects ongoing oscillations (Klimesch, 1999). These changes are manifested through altered signal-to-noise ratios in the oscillations (Pijn et al., 1991). Although there are many ways to characterize an individual's cognitive state, a basic distinction can be made between rest (eyes-open and eyes-closed) and task states. Neuroimaging shows differences among these states, with default mode network activation in resting-state functional connectivity studies, and alpha-band power increase during rest (Raichle, 2015; Compston, 2010). Although these states exhibit large changes in neuroimaging, how they affect EEG phase prediction accuracy is currently unknown. The current project compared EEG phase prediction accuracy in the occipital alpha band between rest and task. Parieto-occipital alpha waves are an ideal signal to display these distinctions as they exhibit the highest power of all EEG frequency bands (Lozano-Soldevilla, 2018). The study used data from multiple online dataset repositories and direct solicitation from researchers. The datasets consisted of rest and task datasets. The task datasets employed a variety of cognitive domains (vigilance, executive attention, decision-making, working memory). For prediction, we used the Educated Temporal Prediction Algorithm, a novel, quick, accurate, and parameter-free phase detection algorithm, on each of these datasets to predict phase angles (Shirinpour et al., 2020). We then used a linear mixed effects model to identify the effect of rest and task on EEG phase prediction accuracy. Our results indicated that the eyes-closed resting state gives the highest accuracy (76.4%, 95% CI [75.0%, 77.9%]), followed by task states (74.7%, 95% CI [72.7%, 76.7%]) and then eyes-open resting state (72.81%, 95% CI [72.8%, 73.1%]). EEG signal power and signal-to-noise ratio (SNR) are significant covariates that also increase accuracy. As such, experiments involving real-time phase prediction may benefit from utilizing periods of high power and SNR in an eyes-closed resting condition.