Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/\ncircuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter\nsignals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to\nextend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data\nacquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification\nalgorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning,\nas well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate\nresults are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired\nobjectives and their real-time implementation as compared to other algorithms.
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