Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

RNA Interference: A Natural Immune System of Plants to Counteract Biotic Stressors.

Cells·2019

Same author

Label-free detection of folic acid using a sensitive fluorescent probe based on ovalbumin stabilized copper nanoclusters.

Talanta·2019

Same author

Assessing and predicting changes of the ecosystem service values based on land use/cover change in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China.

The Science of the total environment·2019

Same author

Jasmonate promotes artemisinin biosynthesis by activating the TCP14-ORA complex in <i>Artemisia annua</i>.

Science advances·2019

Same author

[Composition and Predictive Functional Analysis of Rhizosphere Bacterial Communities in Riparian Buffer Strips in the Danjiangkou Reservoir, China].

Huan jing ke xue= Huanjing kexue·2019

Same author

Antibacterial activity and action mechanism of questin from marine <i>Aspergillus flavipes</i> HN4-13 against aquatic pathogen <i>Vibrio harveyi</i>.

3 Biotech·2019

Same journal

LEARNING COMPACT DNN MODELS FOR BEHAVIOR PREDICTION FROM NEURAL ACTIVITY OF CALCIUM IMAGING.

Journal of signal processing systems·2024

Same journal

Signal Processing Techniques for 6G.

Journal of signal processing systems·2023

Same journal

Fine-tuning-based Transfer Learning for Characterization of Adeno-Associated Virus.

Journal of signal processing systems·2023

Same journal

LSTM Network Integrated with Particle Filter for Predicting the Bus Passenger Traffic.

Journal of signal processing systems·2023

Same journal

OpenVVC Decoder Parameterized and Interfaced Synchronous Dataflow (PiSDF) Model: Tile Based Parallelism.

Journal of signal processing systems·2022

Same journal

Prediction of Bus Passenger Traffic using Gaussian Process Regression.

Journal of signal processing systems·2022

See all related articles

Search research articles

Related Experiment Video

Updated: May 17, 2026

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Published on: October 20, 2021

A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine

Fei Zhang¹, Mehdi Aghagolzadeh, Karim Oweiss

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 USA ( feizhang@msu.edu , aghagolz@msu.edu ).

Journal of Signal Processing Systems

|October 11, 2012

Summary

This summary is machine-generated.

This study presents a novel neuroprocessor for wireless Brain Machine Interface (BMI) systems, enabling efficient neural signal processing and compression for clinical applications. The compact, low-power design ensures reliable data transmission, crucial for real-time brain monitoring.

More Related Videos

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

Published on: July 26, 2024

Related Experiment Videos

Last Updated: May 17, 2026

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Published on: October 20, 2021

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

The DREAM Implant: A Lightweight, Modular, and Cost-Effective Implant System for Chronic Electrophysiology in Head-Fixed and Freely Behaving Mice

Published on: July 26, 2024

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Wireless Brain Machine Interface (BMI) systems require high reliability, scalability, and clinical viability.
Chronic implantation of microelectrode arrays in the brain necessitates efficient neural signal conditioning.
Limited wireless telemetry bandwidth poses a significant challenge for transmitting raw neural data.

Purpose of the Study:

To design and implement a neuroprocessor for conditioning raw extracellular neural signals.
To exploit sparse signal representation for efficient data compression.
To demonstrate multimodal processing (monitoring, compression, spike sorting) for diverse experimental scenarios.

Main Methods:

Developed a neuroprocessor utilizing sparse representation of neural signals.
Implemented a rate-dependent compression strategy for wireless transmission.
Integrated multimodal processing capabilities including monitoring, compression, and spike sorting.
Utilized a nano-FPGA for hardware implementation.

Main Results:

Achieved multimodal processing (monitoring, compression, spike sorting) on a 32-channel system.
Demonstrated preservation of neural data fidelity using rate-dependent compression.
Implemented the neuroprocessor on a 5mm×5mm nano-FPGA with 5.19 mW power consumption.
Evaluated optimal design parameters for compression and sorting performance.

Conclusions:

The designed neuroprocessor meets power-size constraints for clinical use in wireless BMIs.
Sparse representation and rate-dependent compression effectively address bandwidth limitations.
The system supports a wide range of real experimental conditions for neural signal processing.