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. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.