Search research articles

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

Related Concept Videos

Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

You might also read

Related Articles

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

Sort by

Same author

Microbiota-derived indole derivatives as anticancer agents: mechanistic insights and major perspectives.

Future microbiology·2026

Same author

Microplastics in simulated digestion: Surface modifications, enzyme interference, and chemical migration.

Food chemistry·2026

Same author

Optimization of polyhydroxyalkanoate biopolymer production from lignocellulosic wood waste using statistical experimental designs.

Scientific reports·2026

Same author

Emerging implications of bacterial biofilm in cancer biology: Recent updates and major perspectives.

Gut microbes reports·2026

Same author

A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.

Biosensors·2026

Same author

Towards identification of novel inhibitors of EGFR mutants through in-silico approach.

Cancer treatment and research communications·2026

Same journal

A computational model of chemically- and mechanically-induced thrombus formation in cerebral aneurysms.

Computers in biology and medicine·2026

Same journal

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Computers in biology and medicine·2026

Same journal

Assessing the robustness of evaluation metrics for synthetic ECG signal quality.

Computers in biology and medicine·2026

Same journal

Integrating stemness and epithelial-mesenchymal transition signatures with machine learning identifies RUNX1 as a therapeutic vulnerability in colorectal cancer.

Computers in biology and medicine·2026

Same journal

Differential regional textural attributes of tongue in normal and acidity patients in the light of traditional Chinese medicine.

Computers in biology and medicine·2026

Same journal

SC-MSDNet: Spatial-consistent multi-view self-distillation for retinal OCT classification.

Computers in biology and medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Dynamic wavelet-based augmentation for enhanced EEG-based imagined speech classification.

Anand Mohan¹, R S Anand¹

¹Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India.

Computers in Biology and Medicine

|June 18, 2026

Summary

This summary is machine-generated.

This study introduces a novel dynamic wavelet basis selection for electroencephalography (EEG) signal augmentation, significantly improving imagined speech decoding accuracy. The method enhances brain-computer interface performance by better handling noisy EEG data.

Keywords:

Electroencephalography Imagined speech Machine learning Optimization Signal processing Wavelet transform

More Related Videos

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

Related Experiment Videos

Last Updated: Jun 20, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

Area of Science:

Neuroscience and Biomedical Engineering
Signal Processing and Machine Learning

Background:

Brain-computer interfaces (BCIs) enable direct neural control of external devices, offering potential for assistive technologies and neurorehabilitation.
Imagined speech decoding, a BCI paradigm, translates silent speech intentions into commands, but faces challenges due to the noisy and non-stationary nature of electroencephalography (EEG) signals.

Purpose of the Study:

To propose and evaluate a dynamic wavelet basis selection augmentation method for enhancing the reliability of imagined speech decoding using EEG.
To improve the classification accuracy of BCIs by enabling models to better handle inherent noise and variability in EEG data.

Main Methods:

A dynamic wavelet basis selection approach was developed, adaptively choosing the most informative wavelet basis per EEG epoch by minimizing wavelet entropy.
Data augmentation was performed by injecting Gaussian noise into selected wavelet coefficients to improve model robustness.
Classified augmented EEG signals using a convolutional neural network with channel-wise excitation mechanisms for enhanced feature learning.

Main Results:

The proposed method achieved up to 98% classification accuracy for the words-vowels combination and a Cohen's kappa of 0.95.
Performance was comparatively lower for full eight-class classification tasks.
The dynamic wavelet augmentation outperformed conventional and static wavelet-based augmentation strategies in intra-subject and cross-validation settings.

Conclusions:

Dynamic wavelet basis selection is an effective augmentation strategy for improving imagined speech decoding with EEG-based BCIs.
The approach enhances model resilience to EEG signal noise and variability, leading to superior classification performance.
This method holds promise for advancing the development of more reliable and effective BCIs for various applications.