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.

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 Causal roles of prefrontal cortex during spontaneous perceptual switching are determined by brain state dynamics

2021 ◽  
Author(s):  
Takamitsu Watanabe
Keyword(s):  
Prefrontal Cortex ◽  
Visual Perception ◽  
Large Scale ◽  
Brain State ◽  
Energy Landscapes ◽  
Cognitive Dynamics ◽  
State Dependent ◽  
Brain States ◽  
Stimulation System ◽  
The Brain

AbstractThe prefrontal cortex (PFC) is thought to orchestrate cognitive dynamics. However, in tests of bistable visual perception, no direct evidence supporting such presumable causal roles of the PFC has been reported. Here, using a novel brain-state-dependent neural stimulation system, we found that three PFC regions—right frontal eye fields and anterior/posterior dorsolateral PFCs (a/pDLPFCs)—have causal effects on perceptual dynamics but the behavioural effects are detectable only when we modulated the PFC activity in brain-state-/state-history-dependent manners. Also, we revealed that the brain-dynamics-dependent behavioural causality is underpinned by transient changes in the brain state dynamics, and such neural changes are determined by structural transformations of hypothetical energy landscapes. Moreover, we identified different functions of the three PFC areas: in particular, we found that aDLPFC enhances the integration of the two PFC-active brain states, whereas pDLPFC promotes the diversity between them. This work resolves the controversy over the PFC roles in spontaneous perceptual switching and underlines brain state dynamics in fine investigations of brain-behaviour causality.Impact statementPrefrontal causal roles are changing during bistable visual perception, which was determined by large-scale brain state dynamics and attributable to hypothetical energy landscapes that underpin the brain state dynamics.

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.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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