This study proposes a Brain-Computer Interface (BCI) based on Motor Imagery (MI) for ankle-foot dorsiflexion training, providing Functional Electrical Stimulation (FES) as a way of NeuroFeedback (NF), in the tibialis anterior muscle. Riemannian geometry is utilized as a feature extraction technique for a more reliable MI-based electroencephalography discrimination, also considering Passive Movement (PM) data. A complete Spinal Cord Injury (SCI) individual tested the BCI during five sessions, each one on a different day, achieving an accuracy around 0.33, greater than the chance level of 0.25, considering that this is a four-class classification system. The mean BCI latency was lower than 240 ms. Significant Relative power changes (R) were observed in the mu (8-12 Hz) band for MI of dorsiflexion of the feet as well as negative R values centered around Cz, suggesting a better MI performance after observing PM as a visual real guide. As a highlight, our BCI calibrated with more reliable MI data, greatly enhanced the mu (8-12 Hz) rhythm modulation in the operation stage more than the high-beta (18-24 Hz) band modulation. These findings are relevant for advancing NF and BCI approaches to restore lower-limb motor functions, as well as enhance neuroplasticity.

Many assistive and rehabilitation Brain Computer Interface (BCI) systems have been developed by using brain imaging techniques through electroencephalogram (EEG). This study aims to estimate the effective connectivity of subjects that received attention training through a BCI-based game system. For this purpose, mathematical methods and computational tools were used here to evaluate the effect of using the BCI for sustained attention training, programmed with different levels of difficulty for two groups of subjects. As a result, it was possible to identify differences in connectivity patterns between both groups, mainly the flow outflow characteristic in the left temporal region of subjects who performed the training using the game programmed with greater difficulty level. With this preliminary result, we concluded that the proposed strategy for sustained attention training by applying different speed control in a BCI-based game resulted in the generation of different patterns, capable of distinguishing the users in the two groups. Our approach and findings can contribute to improving the development of EEG-based BCI technologies in identifying patterns related to the state of attention.

Stroke is a neurological syndrome that may affect upper and lower limbs functions of post-stroke survivors. Brain-Computer Interfaces (BCIs) are becoming as a promising alter-native to help post-stroke patients rehabilitation, although there are very few associated studies and systems being applied in clinical environment. As a novelty, developing a motor imagery (MI) BCI based on pedal end-effector for motor rehabilitation, we propose to combine pedaling MI and passive pedaling into a Calibration phase. As a result, users would activate continuously their central and peripheral mechanisms linked to lower-limbs throughout BCI intervention. We hypothesize that this strategy enables to obtain a better classification model for our BCI by selecting those feature vectors corresponding to pedaling MI closer to real movements. Therefore, it is expected to have a more effective BCI intervention. Preliminary results show that the proposed method may increase the BCI performance. For almost all participants was noted, during MI tasks, a power decreasing over the foot area (Cz location), corresponding mainly to beta frequency bands, specifically for both low (13 to 22 Hz) and high (23 to 30 Hz) beta bands.

This work presents a brain-computer interface (BCI) based on unsupervised methods for conveying control commands to a robotic exoskeleton, in order to provide support to patients with severe motor disability during walking. For this purpose, an adaptive spatial filter based on similarity indices is proposed to preserve the useful information on electroencephalography (EEG) signals. Additionally, a method for feature selection based on the Maximal Information Compression Index (MICI), and the representation entropy (RE) is used, increasing its robustness for uncertain patterns, such as gait planning. Good values of accuracy (ACC > 75%) and false positive rate (FPR<; 10%) were obtained for four subjects. Thus, this BCI based on unsupervised method may be suitable to recognize uncertainty pattern, such as gait planning.

In this paper, a new method for systolic peak detection of pulse wave signals is presented. This method is based on the detection of each peak by the correlation with adaptive Gaussian function template (GFT). The point of maximum correlation is selected as a systolic peak. Its accuracy and noise robustness were evaluated over a set of annotated signals (arterial blood pressure) from CSL database. The GFT method showed an error respect to trained observers less than 5.5 ± 4.2 ms. Furthermore, this method has an error of 8.99 ± 25.09 ms for several noise realization at 9 dB of signal-noise ratio. The results suggest that this method could be used in the measurement of heart rate and pulse transit time, and in the study of autonomic function.