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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Related Experiment Video

Updated: Jun 16, 2026

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

A Feature Extraction Algorithm of Brain Network of Motor Imagination Based on a Directed Transfer Function.

Shuang Ma^1,2, Chaoyi Dong^1,2, Tingting Jia^1,2

¹College of Electric Power, Inner Mongolia University of Technology, Hohhot 010080, China.

Computational Intelligence and Neuroscience

|March 10, 2022

Summary

This summary is machine-generated.

This study enhances electroencephalography (EEG) signal analysis for motor imagery by integrating Directed Transfer Function (DTF) network features with traditional methods. This approach significantly improves the accuracy of classifying left- and right-hand movements.

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Electroencephalography (EEG) is crucial for understanding brain activity.
Accurate feature extraction from EEG signals is vital for brain-computer interfaces.
Distinguishing between left- and right-hand motor imagery EEG signals presents a challenge.

Purpose of the Study:

To propose and validate a novel multichannel correlation analysis method for EEG feature extraction.
To enhance the classification accuracy of left- and right-hand motor imagery EEG signals.
To evaluate the effectiveness of incorporating network information flow features.

Main Methods:

Utilized Directed Transfer Function (DTF) to identify EEG channel connectivity and construct brain networks.
Extracted network information flow features using DTF.
Combined DTF features with traditional Autoregressive (AR) model parameters for an extended feature set.
Employed Support Vector Machine (SVM) for signal classification.
Compared classification performance across different channel counts (2, 10, 32).

Main Results:

The enlarged feature set significantly improved classification accuracy compared to the traditional AR feature set.
The multichannel analysis method proved more effective across different channel configurations.
DTF-extracted network information flow features achieved higher classification performance than AR model features alone.
The study verified the effectiveness of the proposed multichannel correlation analysis method.

Conclusions:

The proposed multichannel correlation analysis method, incorporating DTF-derived network information flow features, enhances EEG signal classification for motor imagery.
This extended feature set offers a more robust approach for distinguishing between left- and right-hand movements.
The findings underscore the value of network analysis in advancing EEG-based brain-computer interfaces.