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.

The therapeutic effects of neurofeedback (NFB) remain controversial. Here we show that visual NFB of parietal electroencephalographic (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. NFB was displayed either as soon as EEG envelope was processed, or with an extra 250 or 500-ms delay. The time course of NFB-induced changes in the alpha rhythm 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. 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..

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Closed-loop Neuroscience is based on the experimental approach where the ongoing brain activity is 3 recorded, processed, and passed back to the brain as sensory feedback or direct stimulation of neural 4 circuits. The artificial closed loops constructed with this approach expand the traditional stimulus-5 response experimentation. As such, closed-loop Neuroscience provides insights on the function of 6 loops existing in the brain and the ways the flow of neural information could be modified to treat 7 neurological conditions. 8 Neural oscillations, or brain rhythms, are a class of neural activities that have been extensively 9 studied and also utilized in brain rhythm-contingent (BRC) paradigms that incorporate closed loops. 10 In these implementations, instantaneous power and phase of neural oscillations form the signal that 11 is fed back to the brain. 12 Here we addressed the problem of feedback delay in BRC paradigms. In many BRC systems, it 13 is critical to keep the delay short. Long delays could render the intended modification of neural 14 activity impossible because the stimulus is delivered after the targeted neural pattern has already 15 completed. Yet, the processing time needed to extract oscillatory components from the broad-band 16 neural signals can significantly exceed the period of oscillations, which puts a demand for algorithms 17 that could minimize the delay. 18 We used EEG data collected in human subjects to systematically investigate the performance of a 19 range of signal processing methods in the context of minimizing delay in BRC systems. We proposed 20 a family of techniques based on the least-squares filter design -a transparent and simple approach, 21 as it required a single parameter to adjust the accuracy versus latency trade-off. Our algorithm 22 performed on par or better than the state-of the art techniques currently used for the estimation of 23 rhythm envelope and phase in closed-loop EEG paradigms. 24A PREPRINT -NOVEMBER 29, 2019 1 Introduction 25 Investigations of neural oscillations have a long history and continue to be an area of intensive research, particularly 26 when such neuroimaging techniques are used as noninvasive electroencephalography (EEG) and magnetoencephalog-27 raphy (MEG), and invasive electrocorticography (ECoG) and stereo EEG (sEEG). 28A plethora of experimental paradigms and relevant analysis methods have been developed for dealing with specific 29 types of neuronal oscillations, including the methods for their induction and suppression [1, 2]. These paradigms fall 30 in one of two categories. In the first category of studies, changes in neural oscillations are investigated that are induced 31 by a variety of stimuli; the stimuli are presented without a consideration of the ongoing brain activity. In the second 32 category [3], a closed-loop design is implemented where the stimuli are selected based on the characteristics of the 33 ongoing brain activity. 34Below, we describe the studies from the second category where the closed loop is form...

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