Comparing the quality of signals recorded with a rapid response EEG and conventional clinical EEG systems.

Objectives: To compare the quality of electroencephalography (EEG) signals recorded with a rapid response EEG system and the signals recorded with conventional clinical EEG recordings.

Methods: We studied the differences between EEG recordings taken with a rapid response EEG system (Ceribell) compared to conventional EEG through two separate set of studies. First, we conducted simultaneous recording on a healthy subject in an experimental laboratory setting where the rapid response EEG and two conventional EEG recording systems (Nihon Kohden and Natus) were used at the same time on the same subject using separate but adjacently placed electrodes.

Prediction of fatigue-related driver performance from EEG data by deep Riemannian model.

Prediction of the drivers' drowsy and alert states is important for safety purposes. The prediction of drivers' drowsy and alert states from electroencephalography (EEG) using shallow and deep Riemannian methods is presented. For shallow Riemannian methods, the minimum distance to Riemannian mean (mdm) and Log-Euclidian metric are investigated, where it is shown that Log-Euclidian metric outperforms the mdm algorithm.