Real-time P300-based BCI in mechatronic control by using a multi-dimensional approach

Abstract

This study presents a P300-based brain-computer interface (BCI) for mechatronic device driving, i.e. without the need for any physical control. The technique is based on a machine learning (ML) algorithm, which exploits a spatio-temporal characterization of the P300, analyses all the discrimination scenarios through a multiclass classification problem. The BCI is composed of the acquisition unit, the processing unit and the navigation unit. The acquisition unit is a wireless 32-channel electroencephalography headset collecting data from six electrodes. The processing unit is a dedicated µPC performing stimuli delivery, ML and classification, leading to the user intention interpretation. The ML stage is based on a custom algorithm (tuned residue iteration decomposition) which trains the classifier on the user-tuned P300 features. The extracted features undergo a dimensionality reduction and are used to define decision boundaries for the real-time classification. The real-time classification performs a functional approach for the features extraction, reducing the amount of data to be analyzed. The Raspberry-based navigation unit actuates the received commands, supporting the wheelchair motion. The experimental results, based on a dataset of seven subjects, demonstrate that the classification chain is performed in 8.16 ms with an accuracy of 84.28 ± 0.87%, allowing the real-time control of the wheelchair.