Real-time low latency estimation of brain rhythms with deep neural networks.

Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.

Cortical and autonomic responses during staged Taoist meditation: Two distinct meditation strategies.

Meditation is a consciousness state associated with specific physiological and neural correlates. Numerous investigations of these correlates reported controversial results which prevented a consistent depiction of the underlying neurophysiological processes. Here we investigated the dynamics of multiple neurophysiological indicators during a staged meditation session. We measured the physiological changes at rest and during the guided Taoist meditation in experienced meditators and naive subjects. We recorded EEG, respiration, galvanic skin response, and photoplethysmography. All subjects followed the same instructions split into 16 stages. In the experienced meditators group we identified two subgroups with different physiological markers dynamics. One subgroup showed several signs of general relaxation evident from the changes in heart rate variability, respiratory rate, and EEG rhythmic activity. The other subgroup exhibited mind concentration patterns primarily noticeable in the EEG recordings while no autonomic responses occurred. The duration and type of previous meditation experience or any baseline indicators we measured did not explain the segregation of the meditators into these two groups. These results suggest that two distinct meditation strategies could be used by experienced meditators, which partly explains the inconsistent results reported in the earlier studies evaluating meditation effects. Our findings are also relevant to the development of the high-end biofeedback systems.

Short-delay neurofeedback facilitates training of the parietal alpha rhythm.

Objective.Feedback latency was shown to be a critical parameter in a range of applications that imply learning. The therapeutic effects of neurofeedback (NFB) remain controversial. We hypothesized that often encountered unreliable results of NFB intervention could be associated with large feedback latency values that are often uncontrolled and may preclude the efficient learning.Approach.We engaged our subjects into a parietal alpha power unpregulating paradigm facilitated by visual NFB based on the individually extracted envelope of the alpha-rhythm at P4 electrode. NFB was displayed either as soon as electroencephalographic (EEG) envelope was processed, or with an extra 250 or 500 ms delay. The feedback training consisted of 15 two-minute long blocks interleaved with 15 s pauses. We have also recorded 2 min long baselines immediately before and after the training.Main results.The time course of NFB-induced changes in the alpha rhythm power clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest.Significance.Here we for the first time show that visual NFB of parietal EEG alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.

Digital filters for low-latency quantification of brain rhythms in real time.

OBJECTIVE: The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off. APPROACH: Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples. MAIN RESULTS: When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold. SIGNIFICANCE: The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist).

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.