Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Heart rate normalization in the analysis of heart rate variability in congestive heart failure.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2010
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 4, 2025

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

2.2K

Low-cost, mobile EEG hardware for SSVEP applications.

M Kancaoğlu1, M Kuntalp1

  • 1Dokuz Eylul University, Graduate School of Natural and Applied Sciences, Department of Biomedical Technologies, Turkey.

Hardwarex
|December 17, 2024
PubMed
Summary

A new, cost-effective electroencephalography (EEG) hardware system was developed using common integrated circuits, overcoming pandemic-related shortages. This accessible system enables students and researchers to explore EEG and steady-state visual evoked potential (SSVEP) signals.

Keywords:
BciBiopotentialEegSsvep

More Related Videos

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor
06:19

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

Published on: January 12, 2018

8.9K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K

Related Experiment Videos

Last Updated: Jun 4, 2025

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

2.2K
Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor
06:19

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

Published on: January 12, 2018

8.9K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K

Area of Science:

  • Biomedical Engineering
  • Neuroscience Instrumentation
  • Signal Acquisition Technology

Background:

  • Global integrated circuit shortages, exacerbated by the COVID-19 pandemic, have hindered the development of biopotential acquisition devices.
  • Existing electroencephalography (EEG) hardware can be expensive and difficult to procure, limiting accessibility for students and early-career researchers.

Purpose of the Study:

  • To design and develop a cost-effective, precise, and accessible 8-channel EEG hardware system using readily available integrated circuits.
  • To create a mobile, head-mounted EEG device suitable for acquiring signals within the 5-30Hz frequency range, with specific channels optimized for steady-state visual evoked potential (SSVEP) experiments.

Main Methods:

  • Designed an 8-channel EEG hardware system incorporating common integrated circuits and a microcontroller unit (MCU).
  • Developed a mobile headset with a 3D-printable enclosure, integrating the hardware board with protective glasses.
  • Incorporated features such as SSVEP-optimized channels, general-purpose input/output (GPIO) pins, buttons, and a digital-to-analog converter (DAC) output.

Main Results:

  • Successfully designed a cost-effective and precise EEG hardware system that is easily accessible from global distributors.
  • The system facilitates the acquisition of 5-30Hz EEG signals and is specifically configured for SSVEP experiments on two channels.
  • The head-mounted design, 3D-printable enclosure, and integrated components offer a versatile platform for EEG research and education.

Conclusions:

  • The developed EEG hardware system provides an affordable and practical solution for building biopotential acquisition devices, circumventing supply chain issues.
  • This accessible platform empowers students and young researchers to gain hands-on experience with EEG signal acquisition and analysis, particularly SSVEP, without significant initial investment.
  • The availability of programming codes further enhances the system's utility for educational and experimental purposes in neuroscience and biomedical engineering.