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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 21, 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

Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces.

Carmen Vidaurre¹, Nicole Krämer, Benjamin Blankertz

¹Machine Learning Group, Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany. vidcar@cs.tu-berlin.de

Neural Networks : the Official Journal of the International Neural Network Society

|August 8, 2009

Summary

This summary is machine-generated.

New Time Domain Parameters outperform traditional band power features for EEG-based Brain-Computer Interfaces, especially when subject-specific data is unavailable or during transitions to feedback.

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The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

The Combination of Transcranial Alternating Current Stimulation and Electroencephalogram

Published on: October 10, 2025

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) commonly utilize feature extraction methods.
Logarithmic band power estimates, often with subject-specific frequency band optimization, are popular but have limitations.

Purpose of the Study:

Introduce and evaluate a novel feature, Time Domain Parameters (TDPs), derived from generalized Hjorth parameters.
Compare TDPs against established band power features under varying BCI operational conditions.

Main Methods:

TDPs were defined as a generalization of Hjorth parameters.
Feature performance was assessed in two scenarios: (1) without prior subject data and (2) during the transition from BCI calibration to feedback.
Comparisons were made against logarithmic band power features using various spatial filters.

Main Results:

In the absence of subject-specific data, TDPs demonstrated superior performance compared to all tested band power features across all spatial filters.
TDPs also showed an advantage during the calibration-to-feedback transition, where signal frequency content can shift.

Conclusions:

Time Domain Parameters offer a robust and effective alternative to traditional band power features for EEG-BCIs.
TDPs provide significant benefits in scenarios with limited or no subject-specific calibration data and during dynamic signal changes.