scholarly journals A framework for closed-loop neurofeedback for real-time EEG decoding

2019 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Troels Wesenberg Kjaer ◽  
Lars Kai Hansen
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Brain Activity ◽  
Time Signal ◽  
Software Framework ◽  
Cognitive State ◽  
Scalp Eeg ◽  
State Classification ◽  
Dry Electrode ◽  
Wide Range

AbstractNeurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroen-cephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter’s needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre- and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

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

2021 ◽  
Author(s):  
Mark Schatza ◽  
Ethan Blackwood ◽  
Sumedh Nagrale ◽  
Alik S Widge
Keyword(s):  
Open Source ◽  
Real Time ◽  
Closed Loop ◽  
Brain Activity ◽  
Loop Control ◽  
Oscillatory Phase ◽  
Wide Range ◽  
Real Time Tracking ◽  
Therapeutic Tools ◽  
And Behavior

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. 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. 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. 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. 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.

scholarly journals Recovery of dynamics and function in spiking neural networks by closed-loop control

2015 ◽  
Author(s):  
Ioannis Vlachos ◽  
Taskin Deniz ◽  
Ad Aertsen ◽  
Arvind Kumar
Keyword(s):  
Neural Networks ◽  
Closed Loop ◽  
Brain Activity ◽  
Open Loop ◽  
Spiking Neural Networks ◽  
Delayed Feedback Control ◽  
Loop Control ◽  
Wide Range ◽  
Underlying Network ◽  
And Function

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks. Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC besides steering the system back to a healthy state, it also recovers the computations performed by the underlying network. Finally, using our theory we isolate the role of single neuron and synapse properties in determining the stability of the closed-loop system.

scholarly journals Real-Time Low-Cost Speed Monitoring and Control of Three-Phase Induction Motor via a Voltage/Frequency Control Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ali Hmidet ◽  
Olfa Boubaker
Keyword(s):  
Real Time ◽  
Induction Motor ◽  
Closed Loop ◽  
Low Cost ◽  
Closed Loop Control ◽  
Loop Control ◽  
Control Approach ◽  
Three Phase ◽  
Wide Range ◽  
Voltage Frequency

In this paper, a new design of a real-time low-cost speed monitoring and closed-loop control of the three-phase induction motor (IM) is proposed. The proposed solution is based on a voltage/frequency (V/F) control approach and a PI antiwindup regulator. It uses the Waijung Blockset which considerably alleviates the heaviness and the difficulty of the microcontroller’s programming task incessantly crucial for the implementation and the management of such complex applications. Indeed, it automatically generates C codes for many types of microcontrollers like the STM32F4 family, also used in this application. Furthermore, it offers a cost-effective design reducing the system components and increasing its efficiency. To prove the efficiency of the suggested design, not only simulation results are carried out for a wide range of variations in load and reference speed but also experimental assessment. The real-time closed-loop control performances are proved using the aMG SQLite Data Server via the UART port board, whereas Waijung WebPage Designer (W2D) is used for the web monitoring task. Experimental results prove the accuracy and robustness of the proposed solution.

An evaluation of real-time cognitive state classification in a harsh operational environment

2007 ◽  
Author(s):  
Michael C. Dorneich ◽  
Santosh Mathan ◽  
Patricia May Ververs ◽  
Stephen D. Whitlow
Keyword(s):  
Real Time ◽  
Cognitive State ◽  
Operational Environment ◽  
State Classification

scholarly journals Laboratory verification of Fast & Furious phase diversity: Towards controlling the low wind effect in the SPHERE instrument

2018 ◽  
Vol 615 ◽  
pp. A34 ◽  
Author(s):  
M. J. Wilby ◽  
C. U. Keller ◽  
J.-F. Sauvage ◽  
K. Dohlen ◽  
T. Fusco ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Wind Effect ◽  
Wind Speeds ◽  
Phase Diversity ◽  
Expected Performance ◽  
Wide Range ◽  
Predictive Simulations

Context. The low wind effect (LWE) refers to a characteristic set of quasi-static wavefront aberrations seen consistently by the SPHERE instrument when dome-level wind speeds drop below 3 ms−1. The LWE produces bright low-order speckles in the stellar point-spread function (PSF), which severely limit the contrast performance of SPHERE under otherwise optimal observing conditions. Aims. In this paper we propose the Fast & Furious (F&F) phase diversity algorithm as a viable software-only solution for real-time LWE compensation, which would utilise image sequences from the SPHERE differential tip-tilt sensor (DTTS) and apply corrections via reference slope offsets on the AO system’s Shack-Hartmann wavefront sensor. Methods. We evaluated the closed-loop performance of F&F on the MITHIC high-contrast test-bench, under conditions emulating LWE-affected DTTS images. These results were contrasted with predictive simulations for a variety of convergence tests, in order to assess the expected performance of an on-sky implementation of F&F in SPHERE. Results. The algorithm was found to be capable of returning LWE-affected images to Strehl ratios of greater than 90% within five iterations, for all appropriate laboratory test cases. These results are highly representative of predictive simulations, and demonstrate stability of the algorithm against a wide range of factors including low image signal-to-noise ratio (S/N), small image field of view, and amplitude errors. It was also found in simulation that closed-loop stability can be preserved down to image S/N as low as five while still improving overall wavefront quality, allowing for reliable operation even on faint targets. Conclusions. The Fast & Furious algorithm is an extremely promising solution for real-time compensation of the LWE, which can operate simultaneously with science observations and may be implemented in SPHERE without requiring additional hardware. The robustness and relatively large effective dynamic range of F&F also make it suitable for general wavefront optimisation applications, including the co-phasing of segmented ELT-class telescopes.

scholarly journals A closed-loop stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

2021 ◽  
Author(s):  
Takayuki Onojima ◽  
Keiichi Kitajo
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Autoregressive Model ◽  
Closed Loop ◽  
Time Estimation ◽  
Sensory Stimulation ◽  
Time Signal ◽  
Real Time System ◽  
Phase Dynamics ◽  
Instantaneous Phase

We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for closed-loop sensory stimulation in electroencephalography (EEG) experiments. The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation. We demonstrate that the performance of our method shows higher accuracy in predicting the EEG phase than the conventional autoregressive model-based method. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated autoregressive model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

scholarly journals CLoSES: A platform for closed-loop intracranial stimulation in humans

2020 ◽  
Author(s):  
Rina Zelmann ◽  
Angelique C. Paulk ◽  
Ishita Basu ◽  
Anish Sarma ◽  
Ali Yousefi ◽  
...  
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Therapeutic Modality ◽  
Patient Specific ◽  
Cognitive State ◽  
Intracranial Stimulation ◽  
Decision Algorithm ◽  
Depth Electrodes ◽  
Neuropsychiatric Diseases ◽  
Epilepsy Monitoring

AbstractTargeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as a therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Specifically, closed-loop direct electrical stimulation tests in humans performed during semi-chronic electrode implantation in patients with refractory epilepsy could help deepen our understanding of basic research questions as well as the mechanisms and treatment solutions for many neuropsychiatric diseases.However, implementing closed-loop systems is challenging. In particular, during intracranial epilepsy monitoring, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. In addition, the system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features. A robust, yet flexible platform is required to perform different tests in a single participant and to comply with clinical settings.In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial EEG signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for certain duration), stimulation is triggered. An added benefit is the flexibility of CLoSES. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. Other features include randomly timed stimulation, which percentage of biomarker detections produce stimulation, and safety refractory periods.CLoSES has been successfully used in twelve patients with implanted depth electrodes in the epilepsy monitoring unit during cognitive tasks, spindle detection during sleep, and epileptic activity detection. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.

scholarly journals Neural Correlates of Trust During an Automated System Monitoring Task: Preliminary Results of an Effective Connectivity Study

2019 ◽  
Vol 63 (1) ◽  
pp. 83-87
Author(s):  
Nathan Sanders ◽  
Sanghyun Choo ◽  
Nayoung Kim ◽  
Chang S. Nam ◽  
Edward P. Fitts
Keyword(s):  
Real Time ◽  
Brain Activity ◽  
Effective Connectivity ◽  
Autonomous Systems ◽  
Neural Correlates ◽  
Automated System ◽  
Cognitive State ◽  
Monitoring Task ◽  
Design Systems ◽  
Future Work

As autonomous systems become more prevalent and their inner workings become more opaque, we increasingly rely on trust to guide our interactions with them especially in complex or rapidly evolving situations. When our expectations of what automation is capable of do not match reality, the consequences can be sub-optimal to say the least. The degree to which our trust reflects actual capability is known as trust calibration. One of the approaches to studying this is neuroergonomics. By understanding the neural mechanisms involved in human-machine trust, we can design systems which promote trust calibration and possibly measure trust in real time. Our study used the Multi Attribute Task Battery to investigate neural correlates of trust in automation. We used EEG to record brain activity of participants as they watched four algorithms of varying reliability perform the SYSMON subtask on the MATB. Subjects reported their subjective trust level after each round. We subsequently conducted an effective connectivity analysis and identified the cingulate cortex as a node, and its asymmetry ratio and incoming information flow as possible indices of trust calibration. We hope our study will inform future work involving decision-making and real-time cognitive state detection.

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