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

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

You might also read

Related Articles

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

Sort by

Same author

Neural Spelling: A Spell-Based BCI System for Language Neural Decoding.

IEEE transactions on bio-medical engineering·2026

Same author

A Hybrid Covert Attention-Augmented Motor Imagery Paradigm for Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026

Same author

Distinct effects of empathy on self-other processing revealed by different behavioral and EEG indices.

Cognitive, affective & behavioral neuroscience·2026

Same author

Biologically-constrained spiking neural network for neuromodulation in locomotor recovery after spinal cord injury.

PLoS computational biology·2026

Same author

Auditory Steady-State Responses and the Effects of Interaural Decoherence and Presence of Vision.

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

Bipolar EEG Power Spectral Density Analysis of Resting-State for Multi-Level Cognitive Impairment Evaluation.

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

Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026

Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026

Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026

Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026

Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026

Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 14, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

An EEG signal smoothing algorithm using upscale and downscale representation.

Tran Hiep Dinh¹, Avinash Kumar Singh², Quang Manh Doan¹

¹Faculty of Engineering Mechanics and Automation, VNU University of Engineering and Technology, 144 Xuan Thuy road, Cau Giay, Hanoi, Vietnam.

Journal of Neural Engineering

|April 30, 2025

Summary

This summary is machine-generated.

A novel algorithm effectively smooths electroencephalogram (EEG) signals by converting them into binary images and extracting skeletons. This method significantly improves cognitive conflict (CC) detection in brain-computer interfaces, especially in noisy data.

Keywords:

cognitive conflict electroencephalogram signal processing skeletonization thinning

More Related Videos

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

Related Experiment Videos

Last Updated: May 14, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection

Published on: May 31, 2024

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

Area of Science:

Signal Processing
Neuroscience
Biomedical Engineering

Background:

Effective electroencephalogram (EEG) signal smoothing is crucial for accurate analysis and brain-computer interface (BCI) applications.
Maintaining original signal features during smoothing is a significant challenge in EEG analysis.
Cognitive conflict (CC) processing using EEG requires robust signal pre-processing techniques.

Purpose of the Study:

To propose a novel EEG signal-smoothing algorithm that preserves signal features.
To evaluate the algorithm's effectiveness in processing cognitive conflict (CC) tasks.
To assess the algorithm's impact on classification accuracy and robustness in EEG analysis.

Main Methods:

The proposed algorithm visualizes EEG signals by increasing line width, converting the representation frame into a binary image.
An effective thinning algorithm is employed to obtain a unit-width skeleton, serving as the smoothed signal.
The algorithm's application in CC processing is evaluated using classification and visual inspection tasks.

Main Results:

The algorithm demonstrates high effectiveness in data fitting, particularly at high noise levels (SNR ≤ 5 dB), with fitting errors of 86.4%-90.4% compared to counterparts.
Pre-processing EEG data with this algorithm significantly boosted the F1 score of state-of-the-art models by over 1%.
Visual inspection tasks confirmed the algorithm's robustness, enabling clear observation of CC peaks like prediction error negativity and error-related positive potential (Pe).

Conclusions:

The proposed EEG signal-smoothing algorithm offers significant advancements in signal processing for BCI applications.
The algorithm enhances classification accuracy and provides robust identification of cognitive conflict-related neural signals.
This approach represents a valuable tool for improving EEG analysis, particularly in noisy environments and for specific cognitive event detection.