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Frequency recognition in an SSVEP-based brain computer interface using empirical mode decomposition and refined

Chi-Hsun Wu¹, Hsiang-Chih Chang, Po-Lei Lee

¹Department of Electrical Engineering, National Central University, No. 300, Jhongda Rd., Jhongli, Taiwan, ROC.

Journal of Neuroscience Methods

|January 4, 2011

Summary

This study introduces an Empirical Mode Decomposition (EMD) and refined Generalized Zero Crossing (rGZC) method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). The approach achieved an average information transfer rate of 36.99 bits/min and 84.63% accuracy in recognizing user commands.

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a promising avenue for human-computer interaction.
Accurate frequency recognition of SSVEPs is crucial for enhancing BCI performance and user command accuracy.

Purpose of the Study:

To develop and evaluate a novel approach using Empirical Mode Decomposition (EMD) and refined Generalized Zero Crossing (rGZC) for precise SSVEP frequency recognition.
To assess the efficacy of the proposed method in a functional BCI system for virtual command activation.

Main Methods:

EEG signals were recorded from the Oz channel during visual stimulation with flickering light-emitting diodes (LEDs) at frequencies from 30-35 Hz.
Empirical Mode Decomposition (EMD) was employed to decompose EEG signals into intrinsic mode functions (IMFs).
Refined Generalized Zero Crossing (rGZC) was used to calculate instantaneous frequencies, identifying SSVEP-related IMFs within a specific frequency band (29.5-35.5 Hz).

Main Results:

The EMD-rGZC method successfully identified SSVEP-related IMFs, correlating with the visual stimulator frequency.
The system achieved an average information transfer rate (ITR) of 36.99 bits/min.
An average accuracy of 84.63% was recorded for virtual command activation across five subjects.

Conclusions:

The proposed EMD and rGZC approach effectively extracts SSVEP data for BCI applications.
This method demonstrates significant potential for improving the performance and reliability of SSVEP-based BCIs.
The study validates the capability of EMD in processing SSVEP signals for BCI systems.