Juan Antonio Barios

Task-oriented rehabilitation is essential for hand function recovery in stroke patients, and recent advancements in BCI-controlled exoskeletons and neural biomarkers — such as post-movement beta rebound (PMBR) — offer new pathways to optimize these therapies. Movement-related EEG signals from the sensorimotor cortex, particularly PMBR (post-movement) and event-related desynchronization (ERD, during movement), exhibit high task specificity and correlate with stroke severity. This study evaluated PMBR in 34 chronic stroke patients across two cohorts, along with a control group of 16 healthy participants, during voluntary and exoskeleton-assisted movement tasks. Longitudinal tracking in the second cohort enabled the analysis of PMBR changes, with EEG recordings acquired at three timepoints over a 30-session rehabilitation program. Findings revealed significant PMBR alterations in both passive and active movement tasks: patients with severe impairment lacked a PMBR dipole in the ipsilesional hemisphere, while moderately impaired patients showed a diminished response. The marked differences in PMBR patterns between stroke patients and controls highlight the extent of sensorimotor cortex disruption due to stroke. ERD showed minimal task-specific variation, underscoring PMBR as a more reliable biomarker of motor function impairment. These findings support the use of PMBR, particularly the PMBR/ERD ratio, as a biomarker for EEG-guided monitoring of motor recovery over time during exoskeleton-assisted rehabilitation.

Health personnel are often unavailable for supervised robot-aided neurorehabilitation in hospitals, and patients are usually challenged by transportation issues to get to hospital. Thus, a discontinuity between therapy in hospital and at home appears slowing down the upper extremity mobility recovery. The aim of this work was to develop a system, based on wearable devices and EMG armband, able to assess the quality of the upper extremity joint movements and intelligently guide the patients during a home-based rehabilitation. This system fuses a classification model together with a dynamic time warping analysis. The classification model was trained with upper extremity joint movements gathered from clinicians, obtaining more than 80% of accuracy using only five joint angles. Then, the system was tested in two post-stroke patients and a healthy subject. The results suggest that the proposed system can be: (i) a useful tool for clinicians to evaluate the rehabilitation therapy; and (ii) an intelligent system able to make decision based on the quality of the activity executed at home.

Approaches to control epilepsy, one of the most important idiopathic brain disorders, are of great importance for public health. We have previously shown that in sympathetic neurons the neuronal isoform of the serum and glucocorticoid-regulated kinase (SGK1.1) increases the M-current, a well-known target for seizure control. The effect of SGK1.1 activation on kainate-induced seizures and neuronal excitability was studied in transgenic mice that express a permanently active form of the kinase, using electroencephalogram recordings and electrophysiological measurements in hippocampal brain slices. Our results demonstrate that SGK1.1 activation leads to reduced seizure severity and lower mortality rates following status epilepticus, in an M-current–dependent manner. EEG is characterized by reduced number, shorter duration, and early termination of kainate-induced seizures in the hippocampus and cortex. Hippocampal neurons show decreased excitability associated to increased M-current, without altering basal synaptic transmission or other neuronal properties. Altogether, our results reveal a novel and selective anticonvulsant pathway that promptly terminates seizures, suggesting that SGK1.1 activation can be a potent factor to secure the brain against permanent neuronal damage associated to epilepsy.

Assistive technologies aim to increase quality of life, reduce dependence on care giver and on the long term care system. Several studies have demonstrated the effectiveness in the use of assistive technology for environment control and communication systems. The progress of brain-computer interfaces (BCI) research together with exoskeleton enable a person with motor impairment to interact with new elements in the environment. This paper aims to evaluate the environment control interface (ECI) developed under the AIDE project conditions, a multimodal interface able to analyze and extract relevant information from the environments as well as from the identification of residual abilities, behaviors, and intentions of the user. This study evaluated the ECI in a simulated scenario using a two screen layout: one with the ECI and the other with a simulated home environment, developed for this purpose. The sensorimotor rhythms and the horizontal oculoversion, acquired through BCI2000, a multipurpose standard BCI platform, were used to online control the ECI after the user training and system calibration. Eight subjects with different neurological diseases and spinal cord injury participated in this study. The subjects performed simulated activities of daily living (ADLs), i.e. actions in the simulated environment as drink, switch on a lamp or raise the bed head, during ten minutes in two different modes, AIDE mode, using a prediction model, to recognize the user intention facilitating the scan, and Manual mode, without a prediction model. The results show that the mean task time spent in the AIDE mode was less than in the Manual, i.e the users were able to perform more tasks in the AIDE mode during the same time. The results showed a statistically significant differences with p<0.001. Regarding the steps, i.e the number of abstraction levels crossed in the ECI to perform an ADL, the users performed one step in the 90% of the tasks using the AIDE mode and three steps, at least, were necessary in the Manual mode. The user’s intention prediction was performed through conditional random fields (CRF), with a global accuracy about 87%. The environment analysis and the identification of the user’s behaviors can be used to predict the user intention opening a new paradigm in the design of the ECIs. Although the developed ECI was tested only in a simulated home environment, it can be easily adapted to a real environment increasing the user independence at home.