A user-friendly algorithm for adaptive closed-loop phase-locked stimulation

Theoretical and Technical Issues Concerning the Measurement of Alpha Frequency and the Application of Signal Detection Theory: Comment on Buergers and Noppeney (2022)

Classical and recent evidence has suggested that alpha oscillations play a critical role in temporally discriminating or binding successively presented items. Challenging this view, Buergers and Noppeney [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022] found that by combining EEG, psychophysics, and signal detection theory, neither prestimulus nor resting-state alpha frequency influences perceptual sensitivity and bias in the temporal binding task. We propose the following four points that should be considered when interpreting the role of alpha oscillations, and especially their frequency, on perceptual temporal binding: (1) Multiple alpha components can be contaminated in conventional EEG analysis; (2) the effect of alpha frequency on perception will interact with alpha power; (3) prestimulus and resting-state alpha frequency can be different from poststimulus alpha frequency, which is the frequency during temporal binding and should be more directly related to temporal binding; and (4) when applying signal detection theory under the assumption of equal variance, the assumption is often incomplete and can be problematic (e.g., the magnitude relationships between individuals in parametric sensitivity may change when converted into nonparametric sensitivity). Future directions, including solutions to each of the issues, are discussed.

The Effect of the Peristimulus α Phase on Visual Perception through Real-Time Phase-Locked Stimulus Presentation

The α phase has been theorized to reflect fluctuations in cortical excitability and thereby impose a cyclic influence on visual perception. Despite its appeal, this notion is not fully substantiated, as both supporting and opposing evidence has been recently reported. In contrast to previous research, this study examined the effect of the peristimulus instead of prestimulus phase on visual detection through a real-time phase-locked stimulus presentation (PLSP) approach. Specifically, we monitored phase data from magnetoencephalography (MEG) recordings over time, with a newly developed algorithm based on adaptive Kalman filtering (AKF). This information guided online presentations of masked stimuli that were phased-locked to different stages of the α cycle while healthy humans concurrently performed detection tasks. Behavioral evidence showed that the overall detection rate did not significantly vary according to the four predetermined peristimulus α phases. Nevertheless, the follow-up analyses highlighted that the phase at 90° relative to 180° likely enhanced detection. Corroborating neural parietal activity showed that early interaction between α phases and incoming stimuli orchestrated the neural representation of the hits and misses of the stimuli. This neural representation varied according to the phase and in turn shaped the behavioral outcomes. In addition to directly investigating to what extent fluctuations in perception can be ascribed to the α phases, this study suggests that phase-dependent perception is not as robust as previously presumed, and might also depend on how the stimuli are differentially processed as a result of a stimulus-phase interaction, in addition to reflecting alternations of the perceptual states between phases.

Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE)

BACKGROUND

Closing the loop between brain activity and behavior is one of the most active areas of development in neuroscience. There is particular interest in developing closed-loop control of neural oscillations. Many studies report correlations between oscillations and functional processes. Oscillation-informed closed-loop experiments might determine whether these relationships are causal and would provide important mechanistic insights which may lead to new therapeutic tools. These closed-loop perturbations require accurate estimates of oscillatory phase and amplitude, which are challenging to compute in real time.

NEW METHOD

We developed an easy to implement, fast and accurate Toolkit for Oscillatory Real-time Tracking and Estimation (TORTE). TORTE operates with the open-source Open Ephys GUI (OEGUI) system, making it immediately compatible with a wide range of acquisition systems and experimental preparations.

RESULTS

TORTE efficiently extracts oscillatory phase and amplitude from a target signal and includes a variety of options to trigger closed-loop perturbations. Implementing these tools into existing experiments is easy and adds minimal latency to existing protocols.

COMPARISON WITH EXISTING METHODS

Most labs use in-house lab-specific approaches, limiting replication and extension of their experiments by other groups. Accuracy of the extracted analytic signal and accuracy of oscillation-informed perturbations with TORTE match presented results by these groups. However, TORTE provides access to these tools in a flexible, easy to use toolkit without requiring proprietary software.

CONCLUSION

We hope that the availability of a high-quality, open-source, and broadly applicable toolkit will increase the number of labs able to perform oscillatory closed-loop experiments, and will improve the replicability of protocols and data across labs.

A state space modeling approach to real-time phase estimation

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low-frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets, and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the Open Ephys acquisition system, making it widely available for use in experiments.