Analysis of SSVEP component acquisition from EEG signals for efficient target identification

Abstract

The application of the brain-computer interface (BCI) is massively helpful and advantageous for disabled people. Moreover, BCI is an arrangement of software and hardware interface that provides a direct interaction between the human brain and computer devices. Therefore, in this article, A steady state visual evoked potential (SSVEP)-based BCI system is presented to identify SSVEP components from multi-channel electroencephalogram (EEG) data by minimizing background noise using an adaptive spatial filtering method. Here, the proposed adaptive spatial filtering-based SSVEP component extraction (ASFSCE) model improves reproducibility among multiple trails and identifies targets efficiently by optimizing the Eigenvalue problem. Along with that, the proposed ASFSCE model minimizes computational complexity from O(G²) to to get high target identification accuracy with faster execution. Performance results are measured using the SSVEP dataset. In this dataset, 11 subjects are used to perform experiments and 256-channel EEG data is taken. The efficiency of the proposed ASFSCE model is measured in terms of mean target detection accuracy and mean information transfer rate (ITR) in bits per minute. The average detection accuracy and ITR are evaluated by considering 23 trials for each subject. The obtained detection accuracy is 93.47% and ITR is 308.23 bpm.