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Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass filters, manage...
Seizures: Classification01:13

Seizures: Classification

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Understanding seizures and epilepsy relies on key definitions that help in recognizing, classifying, and managing these disorders. These definitions provide a framework for recognizing, classifying, and managing seizure disorders.DefinitionsA seizure is a sudden, abnormal burst of electrical activity in the brain that can cause changes in awareness, movement, sensation, or behavior, depending on the area involved. Epilepsy is a chronic condition characterized by recurrent, unprovoked seizures,...
Epilepsy and Seizures: Overview01:24

Epilepsy and Seizures: Overview

Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
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Related Experiment Video

Updated: May 9, 2026

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System
06:28

High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

Published on: September 27, 2024

Phase-Synchronization Early Epileptic Seizure Detector VLSI Architecture.

K Abdelhalim, V Smolyakov, R Genov

    IEEE Transactions on Biomedical Circuits and Systems
    |July 16, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a low-power processor for real-time neural signal analysis, crucial for adaptive neural stimulation systems. Its efficient design shows promise for early epileptic seizure detection using electroencephalogram (EEG) data.

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    Related Experiment Videos

    Last Updated: May 9, 2026

    High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System
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    High-Quality Seizure-Like Activity from Acute Brain Slices Using a Complementary Metal-Oxide-Semiconductor High-Density Microelectrode Array System

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    Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

    Published on: December 18, 2016

    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Neuromorphic Engineering

    Background:

    • Adaptive neural stimulation requires real-time processing of neural signals.
    • Existing systems often face power consumption and computational limitations.
    • Accurate magnitude and phase-synchronization are key for effective neural signal analysis.

    Purpose of the Study:

    • To develop a low-power VLSI processor architecture for real-time computation of neural signal magnitude and phase-synchronization.
    • To integrate this processor into a closed-loop implantable microsystem for adaptive neural stimulation.
    • To validate the processor's efficacy in detecting epileptic seizures using human intracranial EEG data.

    Main Methods:

    • Designed a VLSI processor architecture utilizing three CORDIC (Coordinate Rotation Digital Computer) processing cores.
    • Implemented the architecture using shift-and-add operations, avoiding multiplication for efficiency.
    • Synthesized and prototyped a 10-bit processor in 1.2 V 0.13 μm CMOS technology.

    Main Results:

    • The processor utilizes 41,000 logic gates and dissipates 3.6 μW per input pair.
    • Achieved a per-channel throughput of 1.7 kS/s at a 2.5 MHz clock speed.
    • Demonstrated linear power scaling with the number of input channels and sampling rate.
    • Validated efficacy in early epileptic seizure detection on human intracranial EEG data.

    Conclusions:

    • The developed low-power VLSI processor architecture is suitable for real-time neural signal analysis in implantable microsystems.
    • The CORDIC-based design offers significant power efficiency for adaptive neural stimulation applications.
    • The processor's performance in seizure detection highlights its potential clinical utility.