Search research articles

Related Concept Videos

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

Machine Learning Model Combining Ventilatory, Hypoxic, Arousal Domains Across Sleep Better Predicts Adverse Consequences of Obstructive Sleep Apnea.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

SleepSIM: Conditional GAN-based non-REM sleep EEG Signal Generator.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023

Same author

Detection and Characterization of Walking Bouts Using a Single Wrist-Worn Accelerometer in Free-living Conditions.

medRxiv : the preprint server for health sciences·2023

Same author

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives.

Environmental science & technology·2022

Same author

LSTM based GAN Networks for Enhancing Ternary Task Classification Using fNIRS Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021

Same author

Bi-Directional Gated Recurrent Unit Based Ensemble Model for the Early Detection of Sepsis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020

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

Search research articles

Related Experiment Video

Updated: Oct 24, 2025

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data.

Sajila D Wickramaratne¹, Md Shaad Mahmud¹

¹Department of Electrical and Computer Engineering, University of New Hampshire, Durham, NH, United States.

Frontiers in Big Data

|August 16, 2021

Summary

This study enhances Brain-Computer Interface (BCI) accuracy using functional near-infrared spectroscopy (fNIRS) data. A Conditional Generative Adversarial Network (CGAN) augmented data, improving deep learning task classification to 96.67%.

Keywords:

CGAN CNN GAN classification deep learning functional near-infrared spectroscopy

More Related Videos

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Related Experiment Videos

Last Updated: Oct 24, 2025

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

fMRI Validation of fNIRS Measurements During a Naturalistic Task

fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Functional near-infrared spectroscopy (fNIRS) is an economical, non-invasive neuroimaging technique for mapping cortical activity.
fNIRS is suitable for population studies and crucial for Brain-Computer Interface (BCI) task classification.
Deep learning models require substantial data for accurate training, which can be challenging to collect for fNIRS.

Purpose of the Study:

To improve the accuracy of deep learning classifiers for fNIRS-based BCI task classification.
To address the challenge of insufficient data for training deep learning models in fNIRS studies.
To enhance fNIRS data augmentation using generative networks.

Main Methods:

Utilized Conditional Generative Adversarial Networks (CGAN) for data augmentation of fNIRS signals.
Integrated a Convolutional Neural Network (CNN) classifier with the CGAN.
Trained the system to classify tasks: Left Finger Tap, Right Finger Tap, and Foot Tap.

Main Results:

The CGAN-CNN model achieved a high task classification accuracy of 96.67%.
Data augmentation via CGAN effectively improved classifier performance with limited fNIRS samples.
Demonstrated the potential of generative networks in enhancing BCI accuracy.

Conclusions:

The proposed CGAN-CNN system significantly enhances fNIRS-based BCI task classification accuracy.
CGANs are effective for augmenting fNIRS data, overcoming sample size limitations.
This approach offers a promising solution for developing more robust BCIs.