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Related Concept Videos

Open and closed-loop control systems01:17

Open and closed-loop control systems

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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
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Magnetic Field Of A Current Loop01:16

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Force On A Current Loop In A Magnetic Field01:17

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Magnetic forces on wires carrying current are most frequently applied in motors. A DC motor is a device that converts electrical energy into mechanical work. In motors, wire loops are enclosed in a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate. The direction of the current is reversed once the loop's surface area is lined up with the magnetic field, causing a constant torque on the loop. During the process, commutators...
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Torque On A Current Loop In A Magnetic Field01:13

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The most common application of magnetic force on current-carrying wires is in electric motors. These consist of loops of wire, which are placed between the magnets with a magnetic field. When current flows through the loops, the magnetic field applies torque, which causes the shaft to rotate, thus converting electrical energy to mechanical energy.
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The Nucleus01:32

The Nucleus

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The nucleus is a membrane-bound organelle that acts as a control center in a eukaryotic cell. It contains chromosomal DNA, which controls gene expression and precisely regulates the production of proteins within the cell. In contrast, the DNA inside the mitochondria and chloroplast only carries out functions that are specific to those organelles.
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Action Potential: Phases of Stimulation01:28

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The action potential is a complex electrical event that occurs in excitable cells, such as neurons and muscle cells. It consists of several distinct phases, each with specific characteristics.
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Related Experiment Video

Updated: Feb 2, 2026

Microelectrode Guided Implantation of Electrodes into the Subthalamic Nucleus of Rats for Long-term Deep Brain Stimulation
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Microelectrode Guided Implantation of Electrodes into the Subthalamic Nucleus of Rats for Long-term Deep Brain Stimulation

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Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation.

Syed A Shah, Gerd Tinkhauser, Chiung Chu Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
    Summary

    This study used machine learning to detect Parkinson's Disease (PD) tremor from brain signals. This could enable on-demand Deep Brain Stimulation (DBS), reducing side effects and energy use.

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    Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computational Neuroscience

    Background:

    • Deep Brain Stimulation (DBS) is a common treatment for Parkinson's Disease (PD) symptoms.
    • Current continuous DBS therapy presents challenges including side effects and high energy consumption due to fluctuating PD symptoms.
    • There is a need for adaptive DBS systems that can adjust stimulation based on real-time patient needs.

    Purpose of the Study:

    • To investigate the efficacy of a logistic regression-based classifier for identifying rest tremor in PD patients using Local Field Potentials (LFPs).
    • To explore the potential of machine learning for developing on-demand DBS therapy.
    • To assess the feasibility of reducing side effects and energy consumption in DBS for PD.

    Main Methods:

    • Utilized Local Field Potentials (LFPs) recorded from Subthalamic Nucleus DBS electrodes in 7 PD patients.
    • Applied a logistic regression classifier to analyze 36.1 minutes of data using 512 ms non-overlapping windows.
    • Identified key frequency bands (31-45 Hz, 5-7 Hz, 21-30 Hz, 46-55 Hz, 56-95 Hz) with high discriminative power for tremor detection.

    Main Results:

    • The classifier achieved classification accuracy significantly above chance level across all patients.
    • Area Under the Curve (AUC) values ranged from 0.67 to 0.93, indicating robust performance.
    • Specific frequency bands in the LFP signals were identified as highly predictive of rest tremor.

    Conclusions:

    • A machine learning-based classifier can accurately detect PD rest tremor from LFP signals.
    • This approach provides a foundation for developing on-demand DBS systems.
    • On-demand DBS has the potential to optimize treatment efficacy, minimize side effects, and conserve battery power in PD patients.