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Motor imagery (MI) is one of the most common paradigms used in brain-computer interfaces (BCIs). This mental process is defined as the imagination of movement without any motion. In some lower-limb exoskeletons controlled by BCIs, users have to perform MI continuously in order to move the exoskeleton. This makes it difficult to design a closed-loop control BCI, as it cannot be assured that the analyzed activity is not related to motion instead of imagery. A possible solution would be the employment of virtual reality (VR). During VR training phase, subjects could focus on MI avoiding any distraction. This could help the subject to create a robust model of the BCI classifier that would be used later to control the exoskeleton. This paper analyzes if gait MI can be improved when VR feedback is provided to subjects instead of visual feedback by a screen. Additionally, both types of visual feedback are analyzed while subjects are seated or standing up. From the analysis, visual feedback by VR was related to higher performances in the majority of cases, not being relevant the differences between standing and being seated. The paper also presents a case of study for the closed-loop control of the BCI in a virtual reality environment. Subjects had to perform gait MI or to be in a relaxation state and based on the output of the BCI, the immersive first person view remained static or started to move. Experiments showed an accuracy of issued commands of 91.0± 6.7, being a very satisfactory result.

Footwear comfort is one of the determinant factors in a buyout decision. The understanding of which brain patterns are involved in the comfort perception of footwear could be an important element to develop the consumer neuroscience field, and could even help during the development phase of new products. The present paper studies the comfort perception through the electroencephalography analysis of the brain signals of ten subjects during walking. For the analysis, different features were extracted from the subject’s biosignals based on power spectral density attributes and temporal and statistical parameters of the data under analysis. The research compared the features when the subjects were wearing a comfortable and a uncomfortable shoe by size on a treadmill. The results indicate that both kind of shoes could be classified with average accuracies of 84,3% and that an influence of parietal, tempo-parietal and in a minor way frontal lobes was detected. Despite the subject’s dependency on the results, the research demonstrates that a common electrode and feature configuration could be applied keeping the results in an average accuracy of 83,7% and that a reduction to a 12 electrode setup maintains the accuracy at a 78,0% value.