Control of a Simulated Terrestrial Vehicle Through the Real-Time Processing and Classification of Electroencephalogram Data

In recent years and in parallel with advances in commercially available computing power, the brain computer interface (BCI) has emerged as a new use for the information obtained from existing electroencephalogram (EEG) acquisition technologies. This system works as a communication pathway between an individual's central nervous system and a computing unit. The BCI described in this study has as primary directive the control of a simulated vehicle, for which it utilizes both neurological activity associated to an individual's state of focus in the frequency domain, as well as artifacts produced by intentional blinks, found in the time domain. In order to successfully translate the user's intention into commands, the acquired signals are spectrally and temporally pre-processed, features of interest arc extracted by means of the wavelet packet decomposition (WPD), and arc finally fed into a convolutional neural network (CNN) model, which outputs recursive predictions about the user's mental state. The set of these general steps is capable of emitting navigational commands to the vehicle, and at the same time, return a user's focus state related to the task in hand in the form of a visual feedback loop. Tests performed with the complete system, demonstrate that a system as the one proposed is satisfactorily functional, being capable of carrying the user's intent to the simulated vehicle with a latency and margin of error within what's expected for BCI applications based on EEG readings.