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

IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...

You might also read

Related Articles

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

Sort by

Same author

Genomic determinants and an exploratory prognostic model for immunotherapy outcomes in recurrent or metastatic cervical cancer.

The oncologist·2026

Same author

Comprehensive analysis of prognostic biomarkers for immunotherapy response in patients with advanced malignant melanoma.

Translational cancer research·2026

Same author

First-line nanoparticle polymeric micellar paclitaxel with gemcitabine in metastatic pancreatic cancer: a single-arm, prospective, and exploratory study.

Gastroenterology report·2026

Same author

Personalized tumor-informed circulating tumor DNA monitoring for early detection of recurrence in postoperative pancreatic cancer.

Frontiers in oncology·2026

Same author

Correction to: Recurrence risk prediction in resected stage I-III melanoma utilizing circulating tumor DNA.

BMC cancer·2026

Same author

Genetic alterations and aberrant hormonal pathways in thymic epithelial tumors.

BMC cancer·2025

Same journal

Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.

Frontiers in computational neuroscience·2026

Same journal

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026

Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026

Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026

Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026

Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 5, 2026

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

DTCNet: finger flexion decoding with three-dimensional ECoG data.

Fufeng Wang¹, Zihe Luo¹, Wei Lv¹

¹School of Big Data, Zhuhai College of Science and Technology, Zhuhai, China.

Frontiers in Computational Neuroscience

|July 24, 2025

Summary

This summary is machine-generated.

This study introduces a new method for decoding finger movements using electrocorticography (ECoG) signals, significantly improving brain-computer interface (BCI) accuracy. The novel approach enhances the decoding of complex motor commands for neuroprosthetic control.

Keywords:

3D spatio-temporal spectrograms ECoG signals brain-computer interfaces dilated-transposed convolution finger movement trajectories

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

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Related Experiment Videos

Last Updated: May 5, 2026

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Published on: June 26, 2012

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

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Electrocorticography (ECoG) offers high-resolution brain activity recording for Brain-Computer Interfaces (BCIs).
Existing ECoG decoding methods struggle with accurately predicting complex finger movement trajectories and long temporal dependencies.
Challenges include confusing movement information between different fingers, limiting decoding performance.

Purpose of the Study:

To develop a novel decoding method for high-precision ECoG signal analysis.
To improve the decoding accuracy of complex motor commands, specifically multi-finger movement trajectories.
To overcome limitations in current time-series analysis for predicting long sequences in BCI applications.

Main Methods:

Transformed 2D ECoG data into 3D spatio-temporal spectrograms using wavelet transform.
Employed a 1D convolutional neural network with Dilated-Transposed convolution for feature extraction.
Simultaneously extracted channel band features and temporal variations for enhanced decoding.

Main Results:

Achieved state-of-the-art performance in decoding finger movements across three subjects in BCI Competition IV.
For the first time, exceeded 80% correlation coefficient between predicted and actual multi-finger motion trajectories.
Reached a maximum correlation coefficient of 82%, demonstrating superior decoding accuracy.

Conclusions:

The proposed method offers a significant advancement in decoding complex motor commands from ECoG signals.
This approach provides new insights for high-precision brain-machine signal decoding in BCI.
Advances the real-world application of BCI systems in neuroprosthetic control.