This paper investigate the effectiveness of the Empirical Mode Decomposition (EMD) based Power Spectrum analysis (PSA) technique to evaluate the Performance of SSVEP based Brain computer inference (BCI) system in terms of SSEP recognition accuracy and Information transmission rate (ITR). Steady State Visual evoked Potential (SSVEP) is a quasi sinusoidal signal contaminated into recorded EEG signal. The presence of artifacts and spontaneous EEG signal deteriorate the SSVEP Performance. EMD is technique that decomposes the recorded EEG Signal into several oscillating components known as intrinsic mode functions (IMF). The selection of IMF components plays a vital role in recognizing SSVEP signal with high accuracy. Power spectrum density (PSD) as a feature is extracted from the SSVEP Prominent IMF component to recognize the accuracy of SSVEP BCI System. The obtained result compared with the Wavelet-based PSA approach and conventional PSA approach. The result obtained from four subject demonstrate that the improve the SSVEP performance in terms accuracy and ITR about 4.24% and 6.78 bits/minute as compared to DWT-PSA, 6.78% and 10.65 bits/minute as compared to standard PSA respectively.
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Ojha, M.K., Mukul, M.K. A Novel Approach Based on EMD to improve the Performance of SSVEP Based BCI System. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08135-6
- Steady state visual evoked potential (SSVEP)
- Brain computer interface (BCI)
- Empirical mode decomposition (EMD)
- Power spectrum analysis (PSD)
- Discrete wavelet decomposition (DWD)