Automatic Time-Frequency Analysis of MRPs for Mind-controlled Mechatronic Devices

This paper describes the design, implementation and in vivo test of a novel Brain Computer Interface (BCI) for the mechatronic devices control. The method exploits electroencephalogram acquisitions (EEG), and specifically the Movement Related Potentials (MRPs) (i.e., beta and mu rhythms), to actuate the user intention on the mechatronic device. The EEG data are collected by only five wireless smart electrodes positioned on the central and parietal cortex area. The acquired data are analyzed by an innovative single-trial classification algorithm that, with respect to the current state of the art, strongly reduces the training time (Minimum: ~1 h, reached: 10 min), as well as the acquisition time - after stimulus - for a reliable classification (Typical: 4-8 s reached: 2 s). As first step, the algorithm performs an EEG time-frequency analysis in the selected bands, making the data suitable for further computations. The implemented machine learning (ML) stage consists of: (i) dimensionality reduction; (ii) statistical inference-based features extraction (FE); (iii) classification model selection. It is also proposed a dedicated algorithm, the MLE-RIDE, for the dimensionality reduction that, jointly with statistical analyses, digitalize the beta and mu rhythms, performing the features extraction. Finally, the best support vector machine (SVM) model is selected and used in the on-line classification. As proof of concept, two mechatronic devices have been brain-controlled by using the proposed BCI algorithm: a three-finger robotic hand and an acrylic prototype car. The experimental results, obtained with data from 3 subjects (aged 26±1), showed an accuracy on human will wireless detection of 87.4%, in the real-time binary discrimination, with 33.7ms of computation times.