Classification of EEG signal using EACA based approach at SSVEP-BCI

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