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Related Experiment Video

Updated: Oct 17, 2025

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.

Muhammad Ahsan Awais¹, Mohd Zuki Yusoff¹, Danish M Khan^1,2

¹Centre for Intelligent Signal & Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

This summary is machine-generated.

Brain connectivity, specifically partial directed coherence (PDC), significantly improves motor imagery (MI) classification for brain-computer interfaces. A probabilistic neural network classifier achieved 98.65% accuracy using PDC features, outperforming other methods.

Keywords:

DTF KNN PDC PhysioNet motor imagery SVM brain effective connectivity brain–computer interface decision tree probabilistic neural network

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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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Published on: July 14, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Motor imagery (MI)-based brain-computer interfaces (BCIs) offer control over external devices using brain activity.
Identifying specific brain regions for MI is challenging due to inter-region communication during motor tasks.
Brain connectivity analysis offers a promising approach to extract insightful features for BCIs.

Purpose of the Study:

To investigate the efficacy of brain connectivity measures, specifically partial directed coherence (PDC) and directed transfer function (DTF), as novel feature sets for MI classification.
To compare the performance of four classification algorithms (SVM, KNN, decision tree, probabilistic neural network) using these connectivity features.
To validate the use of brain connectivity for improved MI pattern recognition in BCIs.

Main Methods:

Utilized effective connectivity measures (PDC and DTF) as unconventional feature sets for MI classification.
Applied MANOVA-based analysis to identify statistically significant brain connectivity pairs.
Compared four machine learning classifiers (SVM, KNN, decision tree, PNN) on two-class MI data from the PhysioNet EEG database.

Main Results:

The probabilistic neural network (PNN) classifier combined with PDC as a feature set achieved the highest average accuracy of 98.65%.
PDC as a feature set demonstrated superior performance over DTF, yielding 98.65% accuracy with PNN compared to 82.81% with DTF.
Brain connectivity features led to better classification outcomes than conventional features, confirming the activation of multiple brain regions.

Conclusions:

Partial directed coherence (PDC) is a highly effective feature set for motor imagery classification in brain-computer interfaces.
The probabilistic neural network (PNN) is a suitable classifier for processing brain connectivity features in MI-based BCIs.
Brain connectivity analysis provides a robust method for enhancing BCI performance by capturing complex neural interactions.