Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces.

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.

Calculation of the Connected Dominating Set Considering Vertex Importance Metrics.

The computation of a set constituted by few vertices to define a virtual backbone supporting information interchange is a problem that arises in many areas when analysing networks of different natures, like wireless, brain, or social networks. Recent papers propose obtaining such a set of vertices by computing the connected dominating set (CDS) of a graph. In recent works, the CDS has been obtained by considering that all vertices exhibit similar characteristics. However, that assumption is not valid for complex networks in which their vertices can play different roles. Therefore, we propose finding the CDS by taking into account several metrics which measure the importance of each network vertex e.g., error probability, entropy, or entropy variation (EV).

SiDIVS: Simple Detection of Inductive Vehicle Signatures with a Multiplex Resonant Sensor.

This work provides a system capable of obtaining simultaneous inductive signatures of vehicles traveling on a roadway with minimal cost. Based on Time-Division Multiplexing (TDM) with multiple oscillators, one for each inductive loop, the proposed system detects the presence of vehicles by means of a shift in the oscillation period of the selected loop and registers the signature of the detected vehicles by measuring the duration of a fixed number of oscillator pulses. In order to test the system in an actual environment, we implement a prototype that we denote as SiDIVS (Simple Detection of Inductive Vehicle Signatures) and acquire different vehicle inductive signatures under real scenarios. We also test the robustness of the detector by simulating the effect of noise on the signature acquisition.

Home Automation System Based on Intelligent Transducer Enablers.

This paper presents a novel home automation system named HASITE (Home Automation System based on Intelligent Transducer Enablers), which has been specifically designed to identify and configure transducers easily and quickly. These features are especially useful in situations where many transducers are deployed, since their setup becomes a cumbersome task that consumes a significant amount of time and human resources. HASITE simplifies the deployment of a home automation system by using wireless networks and both self-configuration and self-registration protocols. Thanks to the application of these three elements, HASITE is able to add new transducers by just powering them up. According to the tests performed in different realistic scenarios, a transducer is ready to be used in less than 13 s. Moreover, all HASITE functionalities can be accessed through an API, which also allows for the integration of third-party systems. As an example, an Android application based on the API is presented. Remote users can use it to interact with transducers by just using a regular smartphone or a tablet.

Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors.

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

The Efficacy of Motor Imagery Additional to Task-Oriented Training for Children with Developmental Coordination Disorder: A Study Protocol for a Randomized Open-Label Controlled Trial.

BACKGROUND: Developmental Coordination Disorder (DCD) affects school-age children and interferes with the practice of their daily activities. Task-oriented work and motor imagery have shown great efficacy in addressing this problem. OBJECTIVE: This study presents a protocol that seeks to understand the effectiveness of a combined program of both modalities on the motor competence of children susceptible to the presence of DCD. DESING: Randomised controlled trial. METHODS: The participants are children susceptible to the presence of DCD (from 6 to 12 years old) distributed into an intervention group that received a total of 20 protocol sessions and a control group that continued with their usual school routine.

Eye State Detection Using Frequency Features from 1 or 2-Channel EEG.

Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.

A Prototype of EEG System for IoT.

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.