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Top1. Introduction
The Brain-Computer Interface (BCI) is a computational platform that decodes information from the input brain signal for controlling the software interface, physical device, cognitive restoration, sensorimotor facilitation, and cognitive augmentation. The interface between the technology and the human brain is the BCI. The synonyms including the neural prosthetic and brain-machine interface (BMI) are used in the BCI (Rabbani,et al., 2019). The communication within the computer and human thought process is developed with the BCI for assisting the disabled patients with the impaired motor function that occurred due to the injury or disease and without severe mental functions (Ge,et al., 2017). The activity of the brain is modulated for exploiting the subject’s ability with the intentional mental effort, like Motor Imagery (MI). The BCIs are used in treating neurological disorders and to control and communicate (Corsi,et al., 2018). The brain activities are converted into control signals in BCI for instructing the external devices, such as wheelchairs and prosthetic limbs (Khalaf,et al., 2018). The neuromuscular activities are either restored or bypassed in the BCIs (Min,et al., 2010) for the individual with neurological defects that caused motor disparities, like Parkinson’s disease, stroke, and amyotrophic lateral sclerosis. Thus, BCIs are crucial for motor-impaired patients to communicate with the environment through brain signals (Khalaf,et al., 2018). The speech BCI measured the brain signals of the user, and produced speech output in the form of sentences, words, and synthesized speech. The speech BCI is also used as a control signal for the interaction between humans and computers (Rabbani,et al., 2019).
The BCI systems are implemented using different brain-image modalities for communicating with the patients in Laboratory Information System (LIS). Due to the non-invasiveness, portability, reasonable price, and heavy temporal resolution, the EEG (Zhang,et al., 2016; Han,et al., 2019) are commonly used when compared to other neuroimaging tools, like functional magnetic resonance imaging (fMRI), near-infrared spectroscopy (NIRS), and Magneto-Encephalography (MEG; Foldes,et al., 2015; Han,et al., 2019). Compared to non-invasive BCI methods, like fMRI (Chai,et al., 2017; Sitaram,et al., 2009) and fNIRS, the EEG has portability, good temporal resolution, and low cost (Naseer and Hong, 2015; Han,et al., 2019). Brain activity is measured through the fluctuations in voltage that resulted from the action potential of the neuron and the metal electrodes that are positioned on the scalp (Khan,et al., 2014) in EEG. While performing the activity, the neuronal firing occurs in the brain leads to electrical activity that is reflected by the EEG signals. The electrical activity is the voltage differences that are caused due to the postsynaptic potentials in cortical neurons’ cell membrane in various locations on the head surface (Hong,et al., 2018). The common method used as a BCI modality for recording the neuronal signals in the EEG. The signals, like Steady-State Visual Evoked Potentials (SSVEP), Motor Imagery (MI), and P300 invoked potential are frequently used for BCI. These three signals are utilized for controlling quadcopters and wheelchairs.