Instrumentation and measurements for non-invasive EEG-based brain-computer interface

Abstract

Brain-Computer Interface (BCI) is an innovative communication technique mainly used in biomedical applications for assisting devices. In this context, main aim is help people with severe disabilities restore the movement ability, or replace lost motor function controlling external devices, or communicate with other people leading them to become more self-sufficient. The connection between BCI and wireless communication standards brings BCI into the Internet of Things (IoT), giving the opportunity to better interconnect the brain of both able and disable people with the surrounding physical and cyber worlds: it is called the human in the loop paradigm. The combination of BCI and IoT falls within the wider topic of Cognitive IoT technology, an enriched solution for Industry 4.0 and IoT in industry. The integration of the human role in the IoT can lead to several advantages both in human life and in technological progress. As a novel measurement device, BCI has to be widely characterized in order to improve the reliability of the obtained result. In this work, the authors review the scientific challenges of electroencephalography data acquisition for BCI, the parameters influencing its variability, and the processing techniques for a correct feature extraction and data classification. In particular, first the influence factors and the issues of EEG acquisition are reviewed. Then, the most popular devices used for BCI sensing unit are described. Finally, the authors describe the post processing techniques, the feature extraction algorithms, and the the classifier to properly choose the right signal feature.