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An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural

Mehrnaz Kh Hazrati¹, Abbas Erfanian

¹Department of Biomedical Engineering, Iran University of Science and Technology, Iran Neural Technology Centre, Hengam Street, Narmak Tehran 16844, Iran.

Medical Engineering & Physics

|June 1, 2010

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This study introduces an adaptive brain-computer interface (BCI) for controlling hand movements in virtual reality. It achieves effective control without extensive subject training, demonstrating robust performance in real-world applications.

Area of Science:

Neuroscience
Computer Science
Human-Computer Interaction

Background:

Brain-computer interfaces (BCI) offer potential for controlling external devices using neural signals.
Subject training and classifier adaptation are significant challenges in developing effective online BCI systems.
Existing BCI systems often require extensive offline training, limiting their real-world applicability.

Purpose of the Study:

To develop an effective online single-trial EEG-based BCI for hand grasp control in virtual reality.
To investigate the feasibility of achieving satisfactory online BCI performance without offline training.
To address the challenge of signal variability in BCI by employing an adaptive machine learning approach.

Main Methods:

Development of a novel online single-trial EEG-based BCI system.

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Implementation of an adaptive probabilistic neural network (APNN) for classifying time-varying EEG signals.

Evaluation of the BCI system's performance with ten naïve subjects during hand movement imagination tasks in VR.

Main Results:

An average classification accuracy of 75.4% was achieved in the first session with approximately 3 minutes of online training, without offline training.
Sustained high accuracy (81.4% in session 2, 79.0% in session 3, 84.0% in session 8) was observed without further online training or calibration.
The APNN demonstrated robust performance across different sessions and subjects, indicating reliable classification of EEG signals.

Conclusions:

The proposed BCI system enables effective hand grasp control in VR without extensive offline subject training.
The adaptive nature of the APNN allows for robust classification of EEG signals despite day-to-day and subject-to-subject variations.
This research presents a promising step towards practical, real-world BCI applications for motor control.