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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
H. C. Nagaraj
Nitte Meenakshi Institute of Technology
Computer Science & IT Engineering

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Classification of EEG signal using EACA based approach at SSVEP-BCI Ashwini S. R.; H. C. Nagaraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp717-726

Abstract

The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.
Co-Authors Ashwini S. R.