Brain-state dependent brain stimulation: Real-time EEG alpha band analysis using sliding window FFT phase progression extrapolation to trigger an alpha phase locked TMS pulse with 1 millisecond accuracy

2015 ◽  
Vol 8 (2) ◽  
pp. 378-379 ◽  
Author(s):  
Christoph Zrenner ◽  
Johannes Tünnerhoff ◽  
Carl Zipser ◽  
Florian Müller-Dahlhaus ◽  
Ulf Ziemann
Keyword(s):  
Real Time ◽  
Brain Stimulation ◽  
Sliding Window ◽  
Alpha Phase ◽  
Brain State ◽  
Alpha Band ◽  
State Dependent ◽  
Phase Progression ◽  
Eeg Alpha ◽  
Millisecond Accuracy

Brain-state dependent non-invasive brain-stimulation with real-time closed-loop simultaneous EEG/TMS

2016 ◽  
Vol 127 (3) ◽  
pp. e41 ◽  
Author(s):  
C. Zrenner ◽  
J. Tünnerhoff ◽  
C. Zipser ◽  
F. Müller-Dahlhaus ◽  
U. Ziemann
Keyword(s):  
Real Time ◽  
Brain Stimulation ◽  
Closed Loop ◽  
Brain State ◽  
Non Invasive ◽  
State Dependent

scholarly journals Brain-State-Dependent Non-Invasive Brain Stimulation and Functional Priming: A Hypothesis

2014 ◽  
Vol 8 ◽  
Author(s):  
Elena G. Sergeeva ◽  
Petra Henrich-Noack ◽  
MichaÅ‚ Bola ◽  
Bernhard A. Sabel
Keyword(s):  
Brain Stimulation ◽  
Brain State ◽  
Non Invasive ◽  
State Dependent

scholarly journals Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS

2021 ◽  
Vol 15 ◽  
Author(s):  
Pedro Caldana Gordon ◽  
Sara Dörre ◽  
Paolo Belardinelli ◽  
Matti Stenroos ◽  
Brigitte Zrenner ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Real Time ◽  
Information Transfer ◽  
Brain State ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Spatial Filters ◽  
Theta Oscillation ◽  
State Dependent ◽  
Theta Phase

BackgroundTheta-band neuronal oscillations in the prefrontal cortex are associated with several cognitive functions. Oscillatory phase is an important correlate of excitability and phase synchrony mediates information transfer between neuronal populations oscillating at that frequency. The ability to extract and exploit the prefrontal theta rhythm in real time in humans would facilitate insight into neurophysiological mechanisms of cognitive processes involving the prefrontal cortex, and development of brain-state-dependent stimulation for therapeutic applications.ObjectivesWe investigate individual source-space beamforming-based estimation of the prefrontal theta oscillation as a method to target specific phases of the ongoing theta oscillations in the human dorsomedial prefrontal cortex (DMPFC) with real-time EEG-triggered transcranial magnetic stimulation (TMS). Different spatial filters for extracting the prefrontal theta oscillation from EEG signals are compared and additional signal quality criteria are assessed to take into account the dynamics of this cortical oscillation.MethodsTwenty two healthy participants were recruited for anatomical MRI scans and EEG recordings with 18 composing the final analysis. We calculated individual spatial filters based on EEG beamforming in source space. The extracted EEG signal was then used to simulate real-time phase-detection and quantify the accuracy as compared to post-hoc phase estimates. Different spatial filters and triggering parameters were compared. Finally, we validated the feasibility of this approach by actual real-time triggering of TMS pulses at different phases of the prefrontal theta oscillation.ResultsHigher phase-detection accuracy was achieved using individualized source-based spatial filters, as compared to an average or standard Laplacian filter, and also by detecting and avoiding periods of low theta amplitude and periods containing a phase reset. Using optimized parameters, prefrontal theta-phase synchronized TMS of DMPFC was achieved with an accuracy of ±55°.ConclusionThis study demonstrates the feasibility of triggering TMS pulses during different phases of the ongoing prefrontal theta oscillation in real time. This method is relevant for brain state-dependent stimulation in human studies of cognition. It will also enable new personalized therapeutic repetitive TMS protocols for more effective treatment of neuropsychiatric disorders.

scholarly journals MAGIC: a MATLAB Toolbox for External Control of Transcranial Magnetic Stimulation Devices

2018 ◽  
Author(s):  
Forough Habibollahi Saatlou ◽  
Nigel C. Rogasch ◽  
Nicolas A. McNair ◽  
Mana Biabani ◽  
Steven D. Pillen ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Brain Stimulation ◽  
Magnetic Stimulation ◽  
Ad Hoc ◽  
A Priori ◽  
External Control ◽  
Brain State ◽  
Matlab Toolbox ◽  
Variation Of Parameters ◽  
State Dependent

The capacity to externally control transcranial magnetic stimulation (TMS) devices is becoming increasingly important in brain stimulation research. Here we introduce MAGIC (MAGnetic stimulator Interface Controller), an open-source MATLAB toolbox for controlling Magstim and MagVenture stimulators. MAGIC includes a series of MATLAB functions which allow the user to arm/disarm the stimulator, send triggers, change stimulator settings such as amplitude, interpulse intervals, and frequency, and receive stimulator setting information via a serial port connection between a computer and the stimulator. By providing external control capability, MAGIC enables greater flexibility in designing research protocols which require trial-by-trial changes of device settings to realize a priori trial randomization or interactive ad hoc adjustment of parameters during an ongoing experiment. MAGIC thus helps to prevent experimental confounds related to the block-wise variation of parameters and facilitates the integration of TMS with cognitive/sensory tasks, and the development of more adaptive brain state-dependent brain stimulation protocols.

scholarly journals EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities

2018 ◽  
Author(s):  
Natalie Schaworonkow ◽  
Jochen Triesch ◽  
Ulf Ziemann ◽  
Christoph Zrenner
Keyword(s):  
Evoked Potentials ◽  
Real Time ◽  
Motor Evoked Potentials ◽  
Corticospinal Excitability ◽  
Brain State ◽  
Stimulation Intensity ◽  
Negative Peak ◽  
State Dependent ◽  
Wide Range ◽  
Brain States

AbstractBackgroundCorticospinal excitability depends on the current brain state. The recent development of real-time EEG-triggered transcranial magnetic stimulation (EEG-TMS) allows studying this relationship in a causal fashion. Specifically, it has been shown that corticospinal excitability is higher during the scalp surface negative EEG peak compared to the positive peak of µ-oscillations in sensorimotor cortex, as indexed by larger motor evoked potentials (MEPs) for fixed stimulation intensity.ObjectiveWe further characterize the effect of µ-rhythm phase on the MEP input-output (IO) curve by measuring the degree of excitability modulation across a range of stimulation intensities. We furthermore seek to optimize stimulation parameters to enable discrimination of functionally relevant EEG-defined brain states.MethodsA real-time EEG-TMS system was used to trigger MEPs during instantaneous brain-states corresponding to µ-rhythm surface positive and negative peaks with five different stimulation intensities covering an individually calibrated MEP IO curve in 15 healthy participants.ResultsMEP amplitude is modulated by µ-phase across a wide range of stimulation intensities, with larger MEPs at the surface negative peak. The largest relative MEP-modulation was observed for weak intensities, the largest absolute MEP-modulation for intermediate intensities. These results indicate a leftward shift of the MEP IO curve during the µ-rhythm negative peak.ConclusionThe choice of stimulation intensity influences the observed degree of corticospinal excitability modulation by µ-phase. Lower stimulation intensities enable more efficient differentiation of EEG µ-phase-defined brain states.

scholarly journals Real-Time Implementation of EEG Oscillatory Phase-Informed Visual Stimulation Using a Least Mean Square-Based AR Model

2021 ◽  
Vol 11 (1) ◽  
pp. 38
Author(s):  
Aqsa Shakeel ◽  
Takayuki Onojima ◽  
Toshihisa Tanaka ◽  
Keiichi Kitajo
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Loop System ◽  
Ar Model ◽  
Brain State ◽  
Mean Square ◽  
Least Mean Square ◽  
Closed Loop System ◽  
Alpha Oscillations ◽  
State Dependent

It is a technically challenging problem to assess the instantaneous brain state using electroencephalography (EEG) in a real-time closed-loop setup because the prediction of future signals is required to define the current state, such as the instantaneous phase and amplitude. To accomplish this in real-time, a conventional Yule–Walker (YW)-based autoregressive (AR) model has been used. However, the brain state-dependent real-time implementation of a closed-loop system employing an adaptive method has not yet been explored. Our primary purpose was to investigate whether time-series forward prediction using an adaptive least mean square (LMS)-based AR model would be implementable in a real-time closed-loop system or not. EEG state-dependent triggers synchronized with the EEG peaks and troughs of alpha oscillations in both an open-eyes resting state and a visual task. For the resting and visual conditions, statistical results showed that the proposed method succeeded in giving triggers at a specific phase of EEG oscillations for all participants. These individual results showed that the LMS-based AR model was successfully implemented in a real-time closed-loop system targeting specific phases of alpha oscillations and can be used as an adaptive alternative to the conventional and machine-learning approaches with a low computational load.

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