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

RadioMe: an automated home-based radio, music playlist, and diary reminder system: Report on recruitment, music compilation, and listening, and preliminary testing of heart rate activated music.

Frontiers in psychology·2025
Same author

Preferred music listening for people living with dementia: Two home-based case studies discussing compilation process, autobiographical and biophysical responses.

Geriatric nursing (New York, N.Y.)·2024
Same author

The advent of quantum computer music: mapping the field.

Reports on progress in physics. Physical Society (Great Britain)·2024
Same author

Genetic Music System with Synthetic Biology.

Artificial life·2020
Same author

A Method for Growing Bio-memristors from Slime Mold.

Journal of visualized experiments : JoVE·2017
Same author

At the crossroads of evolutionary computation and music: self-programming synthesizers, swarm orchestras and the origins of melody.

Evolutionary computation·2004
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: Sep 7, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.3K

SSVEP-based brain-computer interface for music using a low-density EEG system.

Satvik Venkatesh1, Eduardo Reck Miranda1, Edward Braund1

  • 1Interdisciplinary Centre for Computer Music Research (ICCMR), University of Plymouth, Plymouth, UK.

Assistive Technology : the Official Journal of RESNA
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a brain-computer interface (BCI) for a violinist with motor impairments, enabling music creation at home. The BCI achieved high accuracy, translating lab success to real-world application.

Keywords:
brain–computer interface (BCI)computer musicdry electroencephalogram (EEG)musical compositionmusical performance

More Related Videos

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

2.5K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.2K

Related Experiment Videos

Last Updated: Sep 7, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.3K
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

2.5K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.2K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Individuals with severe motor impairments face challenges in pursuing creative activities like music.
  • Brain-computer interfaces (BCIs) offer a potential solution for restoring functional abilities.

Purpose of the Study:

  • To develop and evaluate a bespoke brain-computer interface (BCI) for a musician with severe motor impairments.
  • To enable the user to perform and compose music at home.
  • To investigate the impact of EEG headset placement and inter-stimulus distance on BCI performance.

Main Methods:

  • Utilized a dry, low-density, wireless electroencephalogram (EEG) headset employing steady-state visually evoked potentials (SSVEP).
  • Employed canonical correlation analysis (CCA) for EEG signal analysis without weight-calibration.
  • Assessed BCI performance for musical performance and composition tasks.

Main Results:

  • Optimized EEG headset placement significantly improved the information transfer rate (ITR).
  • The BCI for musical performance achieved an ITR of 37.59 ± 9.86 bits min⁻¹ and 88.89 ± 10.09% accuracy.
  • The BCI for musical composition achieved an ITR of 14.91 ± 2.87 bits min⁻¹ and 95.83 ± 6.97% accuracy.

Conclusions:

  • The developed BCI successfully enabled a user with severe motor impairments to engage in musical composition at home.
  • This demonstrates the effective translation of BCI technology from laboratory settings to practical, real-world applications.
  • BCIs hold significant potential for enhancing the quality of life and creative expression for individuals with disabilities.