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Detection of Target Frequency from SSVEP Signal Using Empirical Mode Decomposition for SSVEP Based BCI Inference System

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Abstract

This paper describes the effectiveness of feature obtained by power spectrum analysis (PSA) as well as the combined method of empirical mode decomposition (EMD) and PSA for the development of brain–computer interface (BCI) system using steady-state visual evoked potential (SSVEP). Accurate detection of SSVEP response from the recorded EEG signal is a difficult task for a new development of the BCI inference system. The EMD technique is a non-linear method of signal decomposition, which generates several intrinsic mode functions (IMFs) of different flickering frequencies. Prominent IMF signal of SSVEP plays a vital role in the accurate detection of frequency. The proposed method achieves the average detection accuracy of 81.45% over four subjects; in contrast, the conventional method of PSA achieves average detection accuracy of 80.43%. The achieved result indicates that the proposed method out performs state of the art by more than 1.02% over four subjects.

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Authors and Affiliations

  1. Electronics and Communication Engineering, BIT Mesra, Ranchi, Jharkhand, 835215, India

    Mukesh Kumar Ojha & Manoj Kumar Mukul

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  1. Mukesh Kumar Ojha
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  2. Manoj Kumar Mukul
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Correspondence to Mukesh Kumar Ojha.

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Ojha, M.K., Mukul, M.K. Detection of Target Frequency from SSVEP Signal Using Empirical Mode Decomposition for SSVEP Based BCI Inference System. Wireless Pers Commun 116, 777–789 (2021). https://doi.org/10.1007/s11277-020-07738-9

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