Some people with severe disabilities are confined in a state in which communication is virtually impossible, being reduced to communicating with their eyes or using sophisticated systems that translate thoughts into words. The EyeTrackers and Brain-Computer Interfaces (BCIs) are suitable systems for those people but their main drawback is their cost. More affordable devices are capable of detecting voluntary blinks and translating them into a binary signal that allows the selection, for example, of an ideogram on a communication board. We tested four different systems based on infrared, bioelectrical signals (Electro-Oculography (EOG) and Electro-Encephalography (EEG)), and video processing. The experiments were performed by people with/without disabilities and analyzed the systems' performances, usability, and method of voluntary blinking (long blinks or sequence of two short blinks). The best accuracy (99.3%) was obtained using Infrared-Oculography (IR-OG) and the worst with the EEG headset (85.9%) and there was a statistical influence of the technology on accuracy. Regarding the method of voluntary blinking, the use of long or double blinks had no statistical influence on accuracy, excluding EOG, and the time taken to perform double blinks was shorter, resulting in a potentially much faster interface. People with disabilities obtained similar values but with greater variability. The preferred technology and blinking methods were Video-Oculography (VOG) and long blinks, respectively. The several Open-Source Hardware (OSHW) devices have been developed and a new algorithm for detecting voluntary blinks has also been proposed, which outperforms most of the published papers in the reviewed literature.

Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. The performances of the different classes given in the size of memory allocated and execution time (number of clock cycles) were analyzed in the low-cost platform Arduino Genuino. In addition, 11 people took part in an experiment in which they had to implement several exercises and complete a usability test. Sampling rates under 250 Hz (typical for many biomedical applications) makes it feasible to implement filters, sliding windows and Fourier analysis, operating in real time. Participants rated software usability at 70.2 out of 100 and the ease of use when implementing several signal processing applications was rated at just over 4.4 out of 5. Participants showed their intention of using this software because it was percieved as useful and very easy to use. The performances of the library showed that it may be appropriate for implementing small biomedical real-time applications or for human movement monitoring, even in a simple open-source hardware device like Arduino Genuino. The general perception about this library is that it is easy to use and intuitive.

This work aims at demonstrating that it is possible to detect emotions using a single EEG channel with an accuracy that is comparable to that obtained in studies carried out with devices that have a high number of channels. In this article the Neurosky Maindwave device, which only a single electrode at the FP1 position, the MatLab and the IBM SPSS Modeler were used to acquire, process and classify the signals respectively. It is remarkable the accuracy achieved in relation to the inexpensive hardware employed for the acquisition of the EEG signal. The result of this study allows us to determine when the brain response is more intense after undergoing the subject, in the experimentation, to the stimuli that generate those emotions. This let us decide which brain power bands are most significants and which moments are the most appropriate to carry out this detection of emotions.

The analysis of cardiac activity is one of the most common elements for evaluating the state of a subject, either to control possible health risks, sports performance, stress levels, etc. This activity can be recorded using different techniques, with electrocardiogram and photoplethysmogram being the most common. Both techniques make significantly different waveforms, however the first derivative of the photoplethysmographic data produces a signal structurally similar to the electrocardiogram, so any technique focusing on detecting QRS complexes, and thus heartbeats in electrocardiogram, is potentially applicable to photoplethysmogram. In this paper, we develop a technique based on the wavelet transform and envelopes to detect heartbeats in both electrocardiogram and photoplethysmogram. The wavelet transform is used to enhance QRS complexes with respect to other signal elements, while the envelopes are used as an adaptive threshold to determine their temporal location. We compared our approach with three other techniques using electrocardiogram signals from the Physionet database and photoplethysmographic signals from the DEAP database. Our proposal showed better performances when compared to others. When the electrocardiographic signal was considered, the method had an accuracy greater than 99.94%, a true positive rate of 99.96%, and positive prediction value of 99.76%. When photoplethysmographic signals were investigated, an accuracy greater than 99.27%, a true positive rate of 99.98% and positive prediction value of 99.50% were obtained. These results indicate that our proposal can be adapted better to the recording technology.

This paper introduces a novel non-invasive wearable device that can infer whether people are suffering from anxiety or not. The device allows capturing physiological signals such as: electrodermal activity (EDA), skin temperature (ST), electrocardiography (ECG) and photoplethysmography (PPG), and provides an estimation of blood pressure -through the pulse transit time (PTT) technique- and breathing rate (BR) -by analyzing the heart rate variability-. The hardware also includes an SD card to store the signals for offline processing in laboratory tests.