Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

Abstract

Modern achievements accomplished in both cognitive neuroscience and human-machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain-Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes' blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain-Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.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][7][8][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. Theoretical Background BCI TypesA BCI provides an interconnection platform that supports the full duplex communication between the brain and an external device. According to the way that BCIs use to set up the brain-device interconnection, they are classified as non-invasive or invasive. Non-invasive BCIs use electrodes placed on the scalp. They are easy and safe to use, low-cost, portable, and offer a relatively hig...

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