Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Trustworthy AI for personalized glycemic control: a systematic review and critical appraisal of multimodal forecasting and safety-critical closed-loop control.

Reviews in endocrine & metabolic disorders·2026
Same author

AutoBiGluNet: transformer-based time series modeling for blood glucose prediction in Type 1 diabetes patients.

Health information science and systems·2026
Same author

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Neuroinformatics·2026
Same author

GAT-BiGRU: explainable multi-task temporal graph learning for glucose forecasting, hypoglycemia risk, and counterfactual insulin adjustment.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Systematic review of Artificial Intelligence-based methods for glycemic control and risk prediction in intensive care units.

Artificial intelligence in medicine·2026

Related Experiment Video

Updated: Mar 23, 2026

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

3.4K

Consumer-grade EEG devices: are they usable for control tasks?

Rytis Maskeliunas1, Robertas Damasevicius2, Ignas Martisius3

  • 1Multimedia Engineering Department, Kaunas University of Technology , Kaunas , Lithuania.

Peerj
|March 26, 2016
PubMed
Summary

This study evaluated low-cost EEG devices, Emotiv EPOC and Neurosky MindWave. The Emotiv EPOC showed superior performance in attention, relaxation, and blinking recognition tasks compared to the Neurosky MindWave.

Keywords:
BCIConsumer-grade EEGUsability

More Related Videos

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.9K
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

9.7K

Related Experiment Videos

Last Updated: Mar 23, 2026

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

3.4K
Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.9K
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

9.7K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Consumer-grade electroencephalography (EEG) devices offer accessible brain-computer interface (BCI) technology.
  • Challenges include BCI illiteracy, technical limitations, and signal artifacts.
  • Evaluating device performance is crucial for practical BCI applications.

Purpose of the Study:

  • To evaluate and compare the performance of two consumer-grade EEG devices: Emotiv EPOC and Neurosky MindWave.
  • To assess their suitability for control tasks based on attention, relaxation, and eye-blinking recognition.
  • To identify limitations and challenges associated with using these devices.

Main Methods:

  • Experimental evaluation with 10 subjects performing concentration/relaxation and blinking recognition tasks.
  • Statistical analysis of attention and meditation data variability and normality.
  • Comparison of recognition accuracy between Emotiv EPOC and Neurosky MindWave.

Main Results:

  • Both devices exhibited high variability and non-normality in attention/meditation data, complicating control task implementation.
  • Neurosky MindWave achieved less than 50% accuracy for blinking recognition.
  • Emotiv EPOC achieved over 75% accuracy for blinking recognition and demonstrated a ~9% improvement in concentration/relaxation tasks.

Conclusions:

  • The Emotiv EPOC device is more suitable for control tasks utilizing attention/meditation levels or eye blinking compared to the Neurosky MindWave.
  • BCI illiteracy and experimental environment setup remain significant challenges for consumer-grade EEG devices.
  • Further research is needed to overcome limitations for reliable BCI applications.