AI Article Synopsis
- The study focuses on enhancing the detection of steady-state visual evoked potentials (SSVEP) in EEG signals for brain-computer interface (BCI) applications, highlighting the challenges posed by noise and artifacts in the data.
- An improved method called MEMD-CCA, which combines multivariate empirical mode decomposition and canonical correlation analysis, was tested on EEG signals from nine healthy volunteers.
- The findings showed that MEMD-CCA significantly outperformed traditional methods like CCA and TMSI, leading to increased accuracy in SSVEP recognition and demonstrating its potential for better BCI performance.
Article Abstract
Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications.
Approach: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition.
Main Results: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition.
Significance: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.
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| http://dx.doi.org/10.1088/1741-2552/aa6a23 | DOI Listing |
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