Human-computer interaction for augmentative communication using a visual feedback system

Brain-computer interface (BCI) has been considered a positive opportunity for schooling, cognitive learning, and remote contact in medical (e.g., neuronal reintegration).One of the most common uses of a computer is creating a brain-computer interface (BCI). This device can read brain activity signals, analyze them, and send out commands to an output device for execution. A BCI could be controlled by any type of brain signal for the most part. Due to the complexity of a head unit, classification precision is lower, costs are higher, and the work is more complicated. BCI is often challenging to use in everyday life. In this research, a Hybrid BCI using a visual feedback system (HBCI-VFS) proposed a control scheme of two brain impulses that take on electrodes and basic activities, requiring the participant to concentrate on stimulation and eye gaze. The stimulation is used to pick instructions by visually producing a steady-state visual evoked potential (SSVEP). The SSVEP control is calibrated by the single eye gaze (collection confirmation) and both eye gaze (i.e., negation and recollection). The neural network architectures are used to extract and analyze functionality in Fourier transformation and confusion for a short time. The findings show that the proposed device can give 38 instructions with a 3 s window period and 98.92% precision via a single bipolar electroencephalogram (EEG) channel.This study introduces a new BCI model based on SSVEP and eye gaze indications for the Smart home application, which could be helpful for Augmentative Communication. The technique presented in this study can support the other BCI-managed applications with high precision, multiple instructions, and fast response.