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A brain-computer interface (BCI) method provides its customers with control channels which might be independent of the brain’s output channels (i.e., the peripheral nervous technique and muscles) (Wolpaw et al., 2002). Such systems may be utilized as a means for communications and restoration of motor functions (by means of a neuroprosthesis) for individuals with motor issues including amyotrophic lateral sclerosis (ALS) and spinal cord injury, andor folks in the persistent locked-in state (LIS). It may also be employed as a neurorehabilitation tool to improve motor andor cognitive overall performance of such men and women. A typical BCI method consists of five stages (see Figure 1): brain-signal acquisition, preprocessing, function extractionselection, classification, and application interface. In the 1st brain-signal acquisition stage, appropriate signals are acquired utilizing an acceptable brain-imaging modality. Since the acquired signals are ordinarily weak and contain noises (physiological and instrumental) and artifacts, preprocessing is required, which is the second stage. In the third stage, some useful information so known as “features” are extracted. These options, in the fourth stage, are classified working with a appropriate classifier. Ultimately, in the fifthstage, the classified signals are transmitted to a computer or other external devices for producing the preferred handle commands to the devices. In neurofeedback applications, a real-time display of brain activity is desirable, which enables self-regulation of brain functions. Figure 1 depicts a sch.
