Towards Plug and Play and Portable BCIs: Embedding Artifacts Rejection and Machine Learning on Wireless EEG Headset

This paper presents a pioneering feasibility study focusing on the development of an innovative device designed for the smart acquisition of electro-encephalographic (EEG) signals. The perspective device must be engineered to integrate Brain-Computer Interface (BCI) functionalities at headset level. Currently, the functions of EEG headsets are confined to the mere acquisition and transmission of EEG signals due to resource limitations imposed by the onboard microcontroller. Additionally, factors like noisy environments, the use of wet electrodes, and improper electrode setups degrade the performance of BCIs, limiting their adaptability to real-world contexts and relegating them primarily to laboratory settings. This work aims to surmount these challenges by implementing a BCI architecture on the EEG headset micro-controller, harnessing the power of the TinyML paradigm. The objective is to establish a user-friendly “plug and play” system capable of robust operation even in less-than-optimal usage conditions. An embedded BCI system featuring eight dry electrodes, self-setup for EEG headsets, in-headset computation, and testing in uncontrolled environments has been designed and evaluated on six subjects for this feasibility study. In the context of a 12-choice P300 speller matrix classification problem, the implemented embedded system has demonstrated performance levels in alignment with the state of the art, even when subjected to usage conditions not standard for BCIs.