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.

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.

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