Search research articles

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

Correction: Irshad et al. A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer. <i>Sensors</i> 2023, <i>23</i>, 2932.

Sensors (Basel, Switzerland)·2026

Same author

Transformation of artistic style and innovative design of oriental folk patterns based on AIGC Technology-A case study of Zhuxian town new year paintings from China.

PloS one·2026

Same author

AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing.

Scientific reports·2026

Same author

Senior populations' attitude towards virtual assistants: A study using extended technology acceptance model.

Assistive technology : the official journal of RESNA·2026

Same author

Deep Reinforcement Learning for Sustainable Urban Mobility: A Bibliometric and Empirical Review.

Sensors (Basel, Switzerland)·2026

Same author

Lightweight authentication for IoT devices (LAID) in sustainable smart cities.

Scientific reports·2025

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: Aug 9, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification.

Farheen Siddiqui¹, Awwab Mohammad¹, M Afshar Alam¹

¹Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, India.

Diagnostics (Basel, Switzerland)

|February 25, 2023

Summary

This study introduces a deep neural network for subject-independent mental task classification from electroencephalography (EEG) signals. The non-invasive framework achieved 77.62% accuracy, outperforming existing methods for brain-computer interfaces.

Keywords:

deep neural network electroencephalography feature extraction mental task principal component analysis

More Related Videos

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

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

Published on: July 26, 2013

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Related Experiment Videos

Last Updated: Aug 9, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

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

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

Published on: July 26, 2013

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Electroencephalography (EEG) signal analysis is crucial for patients with motor impairments.
Subject-independent frameworks enable mental task identification without subject-specific training data.
Deep learning models excel at analyzing complex spatial and time-series EEG data.

Purpose of the Study:

To propose a deep neural network (DNN) model for classifying imagined mental tasks from EEG signals.
To develop a non-invasive, subject-independent framework for mental task identification.
To evaluate the model's performance on a benchmark EEG dataset.

Main Methods:

EEG signals were spatially filtered using a Laplacian surface.
Principal Component Analysis (PCA) was applied for high-dimensional data reduction and feature extraction.
A deep neural network (DNN) was trained using averaged Power Spectrum Density (PSD) values for cross-subject classification.

Main Results:

The proposed DNN model achieved an accuracy of 77.62% in mental task classification.
The model successfully extracted mental task-specific features from EEG data.
The non-invasive approach demonstrated effectiveness in identifying imagined tasks.

Conclusions:

The developed cross-subject classification framework demonstrates superior performance compared to state-of-the-art algorithms.
The study validates the efficacy of deep learning for accurate mental task identification from EEG signals.
The proposed method offers a promising solution for brain-computer interfaces in individuals with limited mobility.