Abstract
In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays.
Highlights
Brain–Computer Interfaces (BCI) are communication systems that monitor the cerebral activity and translate certain characteristics, corresponding to user intentions, to commands for device control.[1]
Current research is focused on the potential of Electroencephalography (EEG)[4,5,6] techniques to capture the brain activity associated to user intent.[7,8,9]
We include those achieved by the OpenBCI device for comparison
Summary
Brain–Computer Interfaces (BCI) are communication systems that monitor the cerebral activity and translate certain characteristics, corresponding to user intentions, to commands for device control.[1]. Both the Emotiv or NeuroSky devices require the use of accompanying proprietary software
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