Background. Closed loop brain-computer interfaces dynamically adjust stimulation settings and/or timings based upon concurrently measured data. EEG (electroencephalography) is a widely used input. Particularly for closed loop applications in sleep monitoring, Phase Locked Loops have been used to track the narrowband phase of the EEG signal in real-time to provide a reference signal for closing the loop. During sleep, motion artifacts are minimal. However, there are many potential applications of real-time phase tracking of EEG when using more mobile EEG where artifacts are not necessarily negligible. Objective. To evaluate the robustness of PLL based EEG phase tracking when used with artifact corrupted EEG signals. We hypothesize that the intrinsic flywheel action of a PLL means that the tracked phase will not be perturbed by transient artifacts, leading to good tracking performance even in EEG situations without dedicated artifact removal stages being applied. Approach. We tested a PLL algorithm by using single channel (Fp1) EEG data from two datasets, each of which contains both cleaned and artifact contaminated versions of the same underlying EEG signal. We explored whether the PLL has similar phase tracking performance on both versions of the data. The Phase-Locked Value (PLV) was used for evaluating the phase tracking performance. Results. In general, PLV values in excess of 0.7 were obtained for phase tracking performance, irrespective of whether clean or artifact contaminated EEG data was passed to the PLL. Averaged across all EEG frequency bands, we found no statistically significant difference (p < 0.01) between the PLV values generated from clean and contaminated versions of the EEG signals. The phase tracking performance remained stable even as the number of artifacts present increased, with the decrease in average PLV being less than 0.1 even in high artifact cases. Tracking each EEG frequency band in isolation, Delta band tracking was more sensitive to low-frequency and high-amplitude artifacts, such as EOG and jump artifacts, with many PLV values below 0.6 being obtained. Differences in PLV values were much lower for the Theta, Alpha, and Beta bands, with differences increasing in the higher frequency Gamma band. Significance: Robust phase tracking in the presence of artifacts means that dedicated artifact removal processing may not be necessary for closed loop EEG, unless working with Delta or high Gamma band signals. Artifact removal algorithms are computationally complex and resource intensive, and operating without a dedicated removal step may reduce the processing time required, allowing a faster closing of the loop.
