While the BIAPT lab typically uses a 128-channel electroencephalographic (EEG) system, we aim to eventually develop translational systems that can be used in clinical settings for the assessment of consciousness. To this end, we compared the data quality of four popular wearable EEG systems: the Epoc+ from Emotiv, OpenBCI, the DSI-24 from Wearable Sensing and the Quick-30 dry EEG from Cognionics. Using two computationally inexpensive metrics of undirected functional connectivity (phase lag index) and directed functional connectivity (directed phase lag index), we compare the integrity of the phase relationships captured by these wearable systems to those recorded from our high-density research-grade EEG system. We rank the EEG systems according to their performance, and provide a freely available MATLAB toolbox containing all metrics such that researchers and non-experts interested in wearable EEG systems can easily assess the quality of other such systems. We hope that this advances the translation of EEG research into non-laboratory settings. The full paper is available in IEEE Access: “Assessing the Quality of Wearable EEG Systems Using Functional Connectivity.”
Our MATLAB toolbox is available here: http://www.github.com/BIAPT/EEGapp