We present a computationally light real-time algorithm which automatically detects blinks, saccades, and fixations from electro-oculography (EOG) data and calculates their temporal parameters. The method is probabilistic which allows to consider the uncertainties in the detected events. The method is real-time in the sense that it processes the data sample-by-sample, without a need to process the whole data as a batch. Prior to the actual measurements, a short, unsupervised training period is required. The parameters of the Gaussian likelihoods are learnt using an expectation maximization algorithm. The results show the promise of the method in detecting blinks, saccades, and fixations, with detection rates close to 100 %. The presented method is published as an open source tool.
Introducing An Open-Source Tool for Near-Real-Time Global Flash Drought Monitoring with SMAP
