Human-Machine Interfaces (HMI) allow users to interact with different devices such as computers or home elements. A key part in HMI is the design of simple non-invasive interfaces to capture the signals associated with the user’s intentions. In this work, we have designed two different approaches based on Electroencephalography (EEG) and Electrooculography (EOG). For both cases, signal acquisition is performed using only one electrode, which makes placement more comfortable compared to multi-channel systems. We have also developed a Graphical User Interface (GUI) that presents objects to the user using two paradigms—one-by-one objects or rows-columns of objects. Both interfaces and paradigms have been compared for several users considering interactions with home elements.
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.
A brain-computer interface for controlling elements commonly used at home is presented in this paper. It includes the electroencephalography device needed to acquire signals associated to the brain activity, the algorithms for artefact reduction and event classification, and the communication protocol.
In the last few decades, different teaching methodologies have been proposed to promote more active learning of students. However, it is still essential to understand the practical implications of the theoretical concepts incorporating additional methodologies, for example, problem-based learning. This methodology could include practises focused on students' programming or on using appropriate software tools. For more meaningful learning, the use of a graphic tool instead of programming provides more insight into those concepts throughout a simple and practical resource. Taking this into account, a software tool has been developed in addition to theoretical sessions in the context of a subject of the Degree in Computer Science, where the learning objectives are related to the representation and compression of multimedia content. In addition, this tool allows to configure useful exercises for these practical sessions, such that students can experiment and analyse the results from a visual perspective. The results of the surveys show a high degree of satisfaction of students, who highlight that the tool has helped them to better understand the curriculum content. In addition, there are signs of improvement in the academic results corresponding to the evaluation of performance with such software.
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