Towards P300-based mind-control: a non-invasive quickly trained BCI for remote car driving

De Venuto, D., Annese, V. F. and Mezzina, G. (2017) Towards P300-based mind-control: a non-invasive quickly trained BCI for remote car driving. In: Magno, M., Ferrero, F. and Bilas, V. (eds.) Sensor Systems and Software: 7th International Conference, S-Cube 2016, Sophia Antipolis, Nice, France, December 1-2, 2016, Revised Selected Papers. Series: Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (205). Springer: Cham, pp. 15-28. ISBN 9783319615622 (doi: 10.1007/978-3-319-61563-9_2)

Abstract

This paper presents a P300-based Brain Computer Interface (BCI) for the control of a mechatronic actuator (i.e. wheelchairs, robots or even cars), driven by EEG signals for assistive technology. The overall architecture is made up by two subsystems: the Brain-to-Computer System (BCS) and the mechanical actuator (a proof of concept of the proposed BCI is shown using a prototype car). The BCS is devoted to signal acquisition (6 EEG channels from wireless headset), visual stimuli delivery for P300 evocation and signal processing. Due to the P300 inter-subject variability, a first stage of Machine Learning (ML) is required. The ML stage is based on a custom algorithm (t-RIDE) which allows a fast calibration phase (only ~190 s for the first learning). The BCI presents a functional approach for time-domain features extraction, which reduces the amount of data to be analyzed. The real-time function is based on a trained linear hyper-dimensional classifier, which combines high P300 detection accuracy with low computation times. The experimental results, achieved on a dataset of 5 subjects (age: 26 ± 3), show that: (i) the ML algorithm allows the P300 spatio-temporal characterization in 1.95 s using 38 target brain visual stimuli (for each direction of the car path); (ii) the classification reached an accuracy of 80.5 ± 4.1% on single-trial P300 detection in only 22 ms (worst case), allowing real-time driving. For its versatility, the BCI system here described can be also used on different mechatronic actuators.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Annese, Dr Valerio
• Authors: De Venuto, D., Annese, V. F., and Mezzina, G.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Publisher: Springer
• ISBN: 9783319615622
• Published Online: 20 July 2017