Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.

BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will. METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding. RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance. CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.

Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton.

This study explores the use of a brain-computer interface (BCI) based on motor imagery (MI) for the control of a lower limb exoskeleton to aid in motor recovery after a neural injury. The BCI was evaluated in ten able-bodied subjects and two patients with spinal cord injuries. Five able-bodied subjects underwent a virtual reality (VR) training session to accelerate training with the BCI. Results from this group were compared with a control group of five able-bodied subjects, and it was found that the employment of shorter training by VR did not reduce the effectiveness of the BCI and even improved it in some cases. Patients gave positive feedback about the system and were able to handle experimental sessions without reaching high levels of physical and mental exertion. These results are promising for the inclusion of BCI in rehabilitation programs, and future research should investigate the potential of the MI-based BCI system.

Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking.

Introduction: Brain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed. Material and methods: First, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one. Results and discussion: The results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP/min) decreased from 31.8 to 3.9 FP/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP/min and 49.3% NOFP/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP/TP and 7.7 FP/min.

Riemannian classification analysis for model EEG intention speed patterns.

In this paper, the paradigm of the intention of speed changes from EEG signals with Riemannian classifiers methods is studied in 10 subjects. In addition, the best frequency band and how different electrode configurations affect the accuracy of the model are analyzed. In the prediction of the intention to change speed, results of 68.6% were obtained, in the one of only Increase, results of 64.41 % were obtained, and in the one of only Decrease, results of 71.5% were obtained.

Assessing user experience with BMI-assisted exoskeleton in patients with spinal cord injury.

Spinal Cord Injury (SCI) refers to damage to the spinal cord that can affect different body functionalities. Recovery after SCI depends on multiple factors, being the rehabilitation therapy one of them. New approaches based on robot-assisted training offer the possibility to make training sessions longer and with a reproducible pattern of movements. The control of these robotic devices by means of Brain-Machine Interfaces (BMIs) based on Motor Imagery (MI) favors the patient cognitive engagement during the rehabilitation, promoting mechanisms of neuroplasticity. This research evaluates the acceptance and feedback received from patients with incomplete SCI about the usage of a MI-based BMI with a lower-limb exoskeleton. Clinical Relevance- Patients experienced satisfaction when using the exoskeleton and levels of mental and physical workload were withing reasonable limits. In addition results from the BMI were promising for the inclusion of this type of systems in rehabilitation programs.

Decoding of Turning Intention during Walking Based on EEG Biomarkers.

In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: H∞ filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter H∞. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.

Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum.

This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge of tDCS. The ultimate goal of this work is to propose areas of improvement to create more effective stimulation therapies with less variability.

Procedural Learning through Action Observation: Preliminary Evidence from Virtual Gardening Activity in Intellectual Disability.

Intellectual disability (ID) compromises intellectual and adaptive functioning. People with an ID show difficulty with procedural skills, with loss of autonomy in daily life. From an embodiment perspective, observation of action promotes motor skill learning. Among promising technologies, virtual reality (VR) offers the possibility of engaging the sensorimotor system, thus, improving cognitive functions and adaptive capacities. Indeed, VR can be used as sensorimotor feedback, which enhances procedural learning. In the present study, fourteen subjects with an ID underwent progressive steps training combined with VR aimed at learning gardening procedures. All participants were trained twice a week for fourteen weeks (total 28 sessions). Participants were first recorded while sowing zucchini, then they were asked to observe a virtual video which showed the correct procedure. Next, they were presented with their previous recordings, and they were asked to pay attention and to comment on the errors made. At the end of the treatment, the results showed that all participants were able to correctly garden in a real environment. Interestingly, action observation facilitated, not only procedural skills, but also specific cognitive abilities. This evidence emphasizes, for the first time, that action observation combined with VR improves procedural learning in ID.

Selection of Spatial, Temporal and Frequency Features to Detect Direction Changes During Gait.

This paper studies the direction changes during the gait by means of two different distributions of electrodes located in the motor, premotor and occipital areas. The objective is analyzing which areas are involved in the detection of the intention of turning while the person is walking. The signals in both options are characterized with frequency and temporal features and classified following a cross-validation process. A 95% of success rate is achieved when the electrodes are disposed along the motor, premotor and occipital areas.Clinical Relevance- The objective of this study is applying the acknowledgements obtained in the designing of a brain-machine interface (BMI) based in the detection of the intention of the direction change during the gait. This BMI has clinical relevance in the rehabilitation of the gait in patients with motor injuries, assisting the patient to perform the movements as realistic as it is possible.

Usability and acceptance of using a lower-limb exoskeleton controlled by a BMI in incomplete spinal cord injury patients: a case study.

Spinal cord injury (SCI) limits life expectancy and causes a restriction of patient's daily activities. In the last years, robotics exoskeletons have appeared as a promising rehabilitation and assistance tool for patients with motor limitations, as people that have suffered a SCI. The usability and clinical relevance of these robotics systems could be further enhanced by brain-machine interfaces (BMIs), as they can be used to foster patients' neuroplasticity. However, there are not many studies showing the use of BMIs to control exoskeletons with patients. In this work we show a case study where one SCI patient has used a BMI based on motor imagery (MI) in order to control a lower limb exoskeleton that assists their gait.

Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton.

Brain-machine interfaces (BMIs) can improve the control of assistance mobility devices making its use more intuitive and natural. In the case of an exoskeleton, they can also help rehabilitation therapies due to the reinforcement of neuro-plasticity through repetitive motor actions and cognitive engagement of the subject. Therefore, the cognitive implication of the user is a key aspect in BMI applications, and it is important to assure that the mental task correlates with the actual motor action. However, the process of walking is usually an autonomous mental task that requires a minimal conscious effort. Consequently, a brain-machine interface focused on the attention to gait could facilitate sensory integration in individuals with neurological impairment through the analysis of voluntary gait will and its repetitive use. This way the combined use of BMI+exoskeleton turns from assistance to restoration. This paper presents a new brain-machine interface based on the decoding of gamma band activity and attention level during motor imagery mental tasks. This work also shows a case study tested in able-bodied subjects prior to a future clinical study, demonstrating that a BMI based on gamma band and attention-level paradigm allows real-time closed-loop control of a Rex exoskeleton.

Ojo rojo grave: glaucoma agudo / Severe red eye: Acute glaucoma

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Pharmacodynamic Impact of Carboxylesterase 1 Gene Variants in Patients with Congestive Heart Failure Treated with Angiotensin-Converting Enzyme Inhibitors.

BACKGROUND: Variation in the carboxylesterase 1 gene (CES1) may contribute to the efficacy of ACEIs. Accordingly, we examined the impact of CES1 variants on plasma angiotensin II (ATII)/angiotensin I (ATI) ratio in patients with congestive heart failure (CHF) that underwent ACEI dose titrations. Five of these variants have previously been associated with drug response or increased CES1 expression, i.e., CES1 copy number variation, the variant of the duplicated CES1 gene with high transcriptional activity, rs71647871, rs2244613, and rs3815583. Additionally, nine variants, representatives of CES1Var, and three other CES1 variants were examined. METHODS: Patients with CHF, and clinical indication for ACEIs were categorized according to their CES1 genotype. Differences in mean plasma ATII/ATI ratios between genotype groups after ACEI dose titration, expressed as the least square mean (LSM) with 95% confidence intervals (CIs), were assessed by analysis of variance. RESULTS: A total of 200 patients were recruited and 127 patients (63.5%) completed the study. The mean duration of the CHF drug dose titration was 6.2 (SD 3.6) months. After ACEI dose titration, there was no difference in mean plasma ATII/ATI ratios between subjects with the investigated CES1 variants, and only one previously unexplored variation (rs2302722) qualified for further assessment. In the fully adjusted analysis of effects of rs2302722 on plasma ATII/ATI ratios, the difference in mean ATII/ATI ratio between the GG genotype and the minor allele carriers (GT and TT) was not significant, with a relative difference in LSMs of 0.67 (95% CI 0.43-1.07; P = 0.10). Results of analyses that only included enalapril-treated patients remained non-significant after Bonferroni correction for multiple parallel comparisons (difference in LSM 0.60 [95% CI 0.37-0.98], P = 0.045). CONCLUSION: These findings indicate that the included single variants of CES1 do not significantly influence plasma ATII/ATI ratios in CHF patients treated with ACEIs and are unlikely to be primary determinants of ACEI efficacy.

Formaldehído: efectos tóxicos y recomendaciones / Formaldehyde: toxis effects and recomendatoris

Personas de la profesión médica y del área de la salud en general, estudiantes de los departamentos de anatomía normal o patologica, histología etc; o profesores, técnicos, obreros y empleados de estas áreas, utilizan y están en contacto directo con una sustancia que desde hace más de una década está siendo analizada y estudiada para comprobar sus efectos tóxicos; el formaldehído. He podido observar que un gran número de personas expuestas al formaldehído, aunque conocen los efectos mediatos (irritación de la mucosa nasal, lagrimeo profuso, tos etc) desconocen la mayoría de los efectos y daños a la salud que esta sustancia puede ocasionar a largo plazo y como consecuencia de exposiciones repetidas, otras personas están al tanto de los efectos pero no toman las medidas del caso. También sugiero medidas de seguridad tomadas en su mayor parte de los estándares norteamericanos que dicta la Agencia para la Protección del Ambiente (US. EPA) y la Agencia para la salud y seguridad ocupacional (OSHA)

Níveles plasmáticos de un marcador de peroxidación lipídica y de vitamina E en mujeres sanas eumenorreicas, en postmenopáusicas sanas y en primigestas que desarrollaron o no hipertensión inducida / Even plasmatic od a marker of lipid peroxidation and E vitamin in women healthy eumenorreicas, in health postmenopausal and in primigravidas that develope or not induced hypertension

Modificaciones en níveles circulantes de malondialdehido y de vitamina E se han asociaado con hipertensión inducida por embarazo, con diabetes gestacional y con parto pretérmino. Hay solo información fragmentaria aa esstos respectos