Self-paced EEG-based Brain-controlled car in Real-World Enviroment

This study presented the design and online experiments of a self-paced EEG-based BCI for controlling a car in real world environment. This paradigm using two distinct MI tasks to generate a multi-task car control strategy, including start, move forwards, turn left, turn right, move backwards, and stop. The experiment results suggested that a brain-actuated car is possible when it runs in low velocity, approximately 5km/hour in our study. This proposed self-paced EEG-based BCI paradigm could potentially help disabled and paralyzed people to gain more mobility in the future, and moreover, provide a supplementary car driving strategy to healthy people. Introduction Brain-computer interface (BCI) techniques have developed rapidly in the pasting two decades. A BCI is a communication system in which an individual can send commands to the external word by generating specific patterns in brain signals without depending on brain’s output channels of peripheral nerves and muscles [1]. The main purpose for BCIs is to enable communication for people with severe disabilities and neurological conditions [2]. BCI can provide direct access to brain states and therefore, enable improved man-machine interaction for healthy people [3,4]. And various studies have explored the potential of BCI applications in many fields: communication [5], neuroprosthetics [6,7], robots [8-10], mobility control [11,12]. These previous studies have promisingly demonstrated the possibility of BCIs for controlling external devices. Bring the BCIs from the laboratory conditions to real world applications is an important aspect for BCI researching. In the current study, we proposed an asynchronous EEG-based BCI paradigm using two distinct MI tasks to generate a multi-task car control strategy, including start, move forwards, turn left, turn right, move backwards, and stop. To hit our targets, two classifiers need to be trained. Firstly, a control intention classier (CIC) was trained to determine whether the ongoing signal represented rest or MI tasks. Secondly, a left/right classier (LRC) was trained to distinguish between left/right MI tasks. Methods System introduction. The BCI control system mainly included three subsystems: BCI signal processing system, and the car control system. The BCI signal processing system records and analyzes the EEG signals and translates them into control commands. The interface system presents the car states and monitors the commands sent to the car. The car control system receives control commands and converts them into electric signals to drive the car (Fig. 1). 3rd International Conference on Machinery, Materials and Information Technology Applications (ICMMITA 2015) © 2015. The authors Published by Atlantis Press 1395 Network Router Car Laptop Computer TCP/IP Protocol Can Bus Fig. 1 Car control system. Participants. Four healthy subjects who have already got their driving licenses (males, age 22-26 years, with an average age of 24 years) participated in the study, all native Chinese speakers. And none had any known cognitive deficit. Two of the subjects had previous MI BCI experiences, and the other subject was complete novices. All of the subjects signed informed consent to participant after receiving a complete description of this study.