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real time data exchange interface  (MathWorks Inc)


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    MathWorks Inc real time data exchange interface
    Real Time Data Exchange Interface, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1959 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/simulink+real+time/Simulink+Real-Time/pmc12972969-67-13-17
    Average 96 stars, based on 1959 article reviews
    real time data exchange interface - by Bioz Stars, 2026-09
    96/100 stars

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    Article Title: Plasticity of interhemispheric motor cortex connectivity induced by brain state-dependent cortico-cortical paired-associative stimulation.
    Article Snippet: Instantaneous phase was estimated in realtime using a custom-built digital biosignal processor with an algorithm implemented in Simulink Real-Time (Mathworks Ltd, USA, R2017a), for details see previous publications15,17.

    Article Title: Effects of Mechanical Perturbation Magnitude on Human Gait Entrainment
    Article Snippet: This experimental framework, developed using Simulink Real-Time (MathWorks Inc., USA), ensured precise synchronization of the sensors and facilitated seamless data collection in a unified file during the experiments.

    Article Title: Plasticity of interhemispheric motor cortex connectivity induced by brain state-dependent cortico-cortical paired-associative stimulation
    Article Snippet: Instantaneous phase was estimated in real-time using a custom-built digital biosignal processor with an algorithm implemented in Simulink Real-Time (Mathworks Ltd, USA, R2017a), for details see previous publications , .

    Article Title: Spinal Reflex Modulation by a Sensory Neuroprosthesis During Gait in an Individual With Lower Limb Loss
    Article Snippet: The interpulse intervals were kept at 50 ms for consistency with previous work.3,4 Electrical stimulation was controlled via a custom routine developed in Simulink Real-Time (R2020b, MathWorks, Natick, MA).

    Article Title: Plasticity of interhemispheric motor cortex connectivity induced by brain state-dependent cortico-cortical paired-associative stimulation
    Article Snippet: Instantaneous phase was estimated in real-time using a custom-built digital biosignal processor with an algorithm implemented in Simulink Real-Time (Mathworks Ltd, USA, R2017a), for details see previous publications , .

    Extraction:

    Article Title: High accuracy selection of elements of groups using a brain machine interface (BMI)
    Article Snippet: .. A dedicated computer running the xPC real-time operating system with Simulink Real-Time (MathWorks, Natick, MA) received the streaming neural data from the NSPs and performed all signal processing, feature extraction, and decoding. ..

    Software:

    Article Title: Inner speech in motor cortex and implications for speech neuroprostheses
    Article Snippet: .. For T12 sessions prior to December 2024, Simulink Real-time was used for data processing and the Psychophysics Toolbox in MATLAB was used to implement task software. .. An additional Windows computer controlled task starting and stopping and interfaced with the Neuroplex-E system.

    Article Title: Cross-brain transfer of high-performance intracortical speech and handwriting BCIs
    Article Snippet: .. For all T5 sessions, and for T12 sessions conducted prior to December 2024, data was processed using Simulink Real-Time, and task software was implemented with the MATLAB Psychophysics Toolbox . .. An additional Windows computer controlled the starting and stopping of tasks and interfaced with the Neuroplex-E system (Blackrock Microsystems).



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    The overall structure of the proposed QAOA-FLC control system.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: The overall structure of the proposed QAOA-FLC control system.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: Control

    Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Comparison of QAOA-FLC and PID performance under baseline, disturbance, and noise conditions.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: Comparison

    Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Hip joint tracking with fixed and QAOA-FLC under moderate load disturbance RMSE: 0.5179 → 0.0372, Overshoot: 12.6% → 2.9%, transit time: 2.60 → 0.95 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Knee joint tracking with fixed and QAOA-FLC moderate load disturbance, RMSE: 0.3137 → 0.0187, Overshoot: 8.2% → 1.4%, transit time: 2.10 → 0.80 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Ankle joint tracking with fixed and QAOA-FLC under moderate load disturbance, RMSE: 0.3177 → 0.0223, Overshoot: 9.8% → 2.0%, transit time: 2.35 → 0.90 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Hip joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4820 → 0.0415, Overshoot: 11.8% → 3.1%, Transit time: 2.55 → 0.92 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Knee joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.4365 → 0.0398, Overshoot: 10.9% → 2.7%, Transit time: 2.45 → 0.90 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Ankle joint tracking with fixed and QAOA- FLC under severe load disturbance, RMSE: 0.3952 → 0.0441, Overshoot: 9.7% → 2.5%, Transit time: 2.40 → 0.88 s.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 4% sensor noise. Hip RMSE: 0.1729→ 0.0225, Knee RMSE: 0.1144 → 0.0247, Ankle RMSE: 0.0979 → 0.0275.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Joint tracking performance of QAOA-FLC and standard FLC under 40% sensor noise, Hip RMSE: 4.2875→ 0.1605, Knee RMSE: 2.2249 → 0.1857, Ankle RMSE: 1.6203 → 0.2313.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques:

    Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Journal: Scientific Reports

    Article Title: Adaptive intelligent controller for a lower limb rehabilitation robot using QAOA-based online membership optimization

    doi: 10.1038/s41598-026-41647-4

    Figure Lengend Snippet: Total system energy of the 3-DOF rehabilitation robot under FLC and QAOA-FLC.

    Article Snippet: The hardware-in-the-loop simulation was performed using the developed lower-limb rehabilitation robot model and the proposed QAOA-FLC implemented in MATLAB/Simulink ® with real-time interfacing to microcontroller hardware.

    Techniques: