Recognition a Multi-pattern in BCI system Based SSVEPs

Brain-computer Interfaces (BCI) is a significant communication channel, support a handicap people from suffering of disabilities such as amyotrophic lateral sclerosis (ALS). A multi-pattern of visual stimulus based on Steady-state Evoked Potential (SSVEP) is extract a particular stimulation to continuous pragmatic brain response. In this empirical study, which is depend on EEG signals by evoke the brain signals using a multi-patterns stimulation. Exploit the ability of multi-pattern flicker LEDs of visual stimuli based SSVEP foundation to extract the EEG signal features. The human-brain revealed a capability to distinguish between multi-patterns paradigms based on Regular/Irregular stimulus. Analysis of Variance (ANOVA) that utilize as preliminary result to discernment the brain response and demonstrate activity effect in each pattern based SSVEP. Dynamics brain waves are noisy, non-stationary and non-linear; therefore apply Hilbert Transform (HT) to analysis and extract the feature of stimuli responses in two agreements of phases and amplitude on each stimulus pattern. Practically observe a distinction differences between stimuli patterns, which are alert a dynamics wave brain represent different activity states. Differences of multi-patterns effects and effort of the brain activity are significant distinguishable between each other's which is demonstrated in our results. Keywords— Brain-computer interface (BCI);Steady-state Evoked Potential (SSVEP);fast Fourier transform (FFT);Hilbert transform (HT);Phase-tagged trigger (PTT).