**1. Introduction**

Communication within the body of mammals takes place via both electrical and chemical signals. *Electrophysiology* is the branch of physiology that studies the electrical activities which are associated with bodily parts. The recording of electrophysiological data is performed by placing electrodes at the corresponding areas of interest. By this method, there are numerous systems developed which are able to monitor the electrical activity and corresponding electrophysiological data in various organs such as heart, brain, eyes, muscles, and stomach [1–3].

*Electroencephalography* (EEG) is an electrophysiological method which is used in order to monitor the electrical activity of the brain by placing electrodes on the external surface of the scalp. EEG records variations of voltage caused by the flow of ionic current in the interior of the brain's neurons. Therefore, EEG signals are waveforms, also known as brainwaves or brain waveforms, which signify the neural oscillations produced by neurons which intercommunicate. Brainwaves are detected in the frequency domain, having signal intensity measured in microvolts (μV) and signal frequency usually ranging from 1 to 100 Hz. According to their frequency, there are specific bands classified as delta (δ) (1–4 Hz), theta (θ) (4–7 Hz), alpha ( α) (8–13 Hz), beta (β) (13–30 Hz), and gamma ( γ) (>30 Hz) [4].

A *Brain–Computer Interface* (BCI) is a system that enables communication between brain and machines. A BCI, in order to perform its purposes, records brain signals, interprets them, and produces corresponding commands to a connected machine [5]. BCI technology is used in various applications, such as security and authentication, education, neuromarketing and advertisement, games and entertainment, and several medical applications, such as cognitive neuroscience, brain-related prevention and diagnosis of health problems, rehabilitation, and restoration [6–9].

This article presents the development of a BCI-based system that performs the motion control of a robotic vehicle by using brainwaves of a human operator. After capturing the brainwaves via EEG, a set of features is extracted and given as input to a neural network, which is trained to predict the desired movement of the robotic vehicle. The rest of this paper is organized as follows: In Section 2, the theoretical background of the research carried out is set up. In Section 3, the structure and operation of the proposed system are explained. In Section 4, the performance of the system is evaluated through the description of the experimental tests made, and the presentation of the corresponding results and discussion on them. Finally, Section 5 concludes the article and proposes future research work.
