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This article shows the lessons learned from clinical trials of a new neurorehabilitation therapy for cerebro-vascular accident (CVA) patients. The new therapy is based on the combination of a transcranial direct current stimulation (tDCS) strategy, a brain–machine interface (BMI) based on electroencephalographic signals, and a pedaling system. The new therapy was applied during five consecutive days to six CVA patients with motor limitations in their right lower limb that are in the subacute phase. In this article, not only the clinical results are presented, but also the experiences and challenges of performing clinical trials with patients are described, which can be of great interest for future researchers that are planning to evaluate BMIs and tDCS systems with patients.

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.

Averaging electromyographic activity prior to muscle synergy computation is a common method employed to compensate for the inter-repetition variability usually associated with this kind of physiological recording. Capturing muscle synergies requires the preservation of accurate temporal and spatial information for muscle activity. The natural variation in electromyography data across consecutive repetitions of the same task raises several related challenges that make averaging a non-trivial process. Duration and triggering times of muscle activity generally vary across different repetitions of the same task. Therefore, it is necessary to define a robust methodology to segment and average muscle activity that deals with these issues. Emerging from this need, the present work proposes a standard protocol for segmenting and averaging muscle activations from periodic motions in a way that accurately preserves the temporal and spatial information contained in the original data and enables the isolation of a single averaged motion period. This protocol has been validated with muscle activity data recorded from 15 participants performing elbow flexion/extension motions, a series of actions driven by well-established muscle synergies. Using the averaged data, muscle synergies were computed, permitting their behavior to be compared with previous results related to the evaluated task. The comparison between the method proposed and a widely used methodology based on motion flags, shown the benefits of our system maintaining the consistency of muscle activation timings and synergies.

This letter describes a man-machine bimodal interface that is based on the combination of visual control, through electrooculography, and manual control, using a desktop input device that is operated with the hand. This letter introduces design concepts for this bimodal interface with the goal to support those that cannot use desktop device controls, those that require support with desktop device controls, as well as those with normative capabilities but who wish to enhance speed performance. To that end, several control strategies have been designed. Each control strategy has been tested in applications with a real robot arm. The results indicate that such bimodal interfaces show promise to support a range of motor limitations.

This paper describes a new portable and wireless interface based on electrooculography (EOG) aimed at people with severe motor disorders. This interface allows us detecting the movement of the eyes measuring the potential between the cornea and the retina. The interface uses five electrodes placed around the eyes of the user in order to register this potential. A processing algorithm of the EOG signals has been developed in order to detect the movement of the eyes. This interface has many advantages in comparison to commercial devices. It is a cheap and small sized device with USB compatibility. It does not need power supply from the network as it works with batteries and USB supply. Several experiments have been done to test the electronics of the interface. A first set of experiments has been performed to obtain the movement of the eyes of the user processing the signals provided by the interface. In addition, the interface has been used to control a real robot arm. The accuracy and time taken have been measured showing that the user is capable of controlling the robot using only his/her eyes with satisfactory results.