RESUMO

Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

RESUMO

Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic systems, such as motorized mini exercise bikes (MMEBs), have been demonstrated to be suitable for restoring gait-related functions. However, kinematic estimation of continuous motion in BCI systems based on electroencephalography (EEG) remains a challenge for the scientific community. This study proposes a comparative analysis to evaluate two artificial neural network (ANN)-based decoders to estimate three lower-limb kinematic parameters: x- and y-axis position of the ankle and knee joint angle during pedaling tasks. Long short-term memory (LSTM) was used as a recurrent neural network (RNN), which reached Pearson correlation coefficient (PCC) scores close to 0.58 by reconstructing kinematic parameters from the EEG features on the delta band using a time window of 250 ms. These estimates were evaluated through kinematic variance analysis, where our proposed algorithm showed promising results for identifying pedaling and rest periods, which could increase the usability of classification tasks. Additionally, negative linear correlations were found between pedaling speed and decoder performance, thereby indicating that kinematic parameters between slower speeds may be easier to estimate. The results allow concluding that the use of deep learning (DL)-based methods is feasible for the estimation of lower-limb kinematic parameters during pedaling tasks using EEG signals. This study opens new possibilities for implementing controllers most robust for MMEBs and BCIs based on continuous decoding, which may allow for maximizing the degrees of freedom and personalized rehabilitation.

RESUMO

This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, using multiclass classification based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and minimum-norm estimate algorithms were implemented to select discriminant channels and enhance the EEG data. Hence, deep EEGNet and convolutional recurrent neural networks were separately implemented to classify the EEG data for image visualization into 40 labels. Using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures. The satisfactory results obtained with this method offer a new implementation opportunity for multitask embedded BCI applications utilizing a reduced number of both channels (<50%) and network parameters (<110 K).

Assuntos

RESUMO

Objective:Electroencephalography signals are frequently used for various Brain-Computer interface (BCI) tasks. While deep learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data from multiple subjects, transfer learning enables more effective training of DL models. A technique that is gaining popularity is Euclidean alignment (EA) due to its ease of use, low computational complexity, and compatibility with DL models. However, few studies evaluate its impact on the training performance of shared and individual DL models. In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals.Approach:We used EA as a pre-processing step to train shared DL models with data from multiple subjects and evaluated their transferability to new subjects.Main results:Our experimental results show that it improves decoding in the target subject by 4.33% and decreases convergence time by more than 70%. We also trained individual models for each subject to use as a majority-voting ensemble classifier. In this scenario, using EA improved the 3-model ensemble accuracy by 3.71%. However, when compared to the shared model with EA, the ensemble accuracy was 3.62% lower.Significance:EA succeeds in the task of improving transfer learning performance with DL models and, could be used as a standard pre-processing technique.

Assuntos

RESUMO

Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.

RESUMO

5SO3H-8-hydroxyquinoline coordinated to Europium (Eu-5SO3-HQ) was incorporated in biomembrane models using Langmuir monolayers. Dipalmitoyl phosphatidylcholine (DPPC) and dipalmitoyl phosphatidylserine (DPPS) were employed, representing mammalian cells and dioctadecyldimethylammonium bromide (DODAB) as a positively charged lipid to study the contrast with negatively charged lipids. Tensiometric, rheological and spectroscopic techniques were employed to characterize Eu-5SO3-HQ- lipid monolayer interactions. The complex condenses all the monolayer indicating interactions with the lipids' polar heads, but with distinctive effects on the mechanical and rheological properties. While the complex decreases the compression and elastic moduli of DPPC and DPPS monolayers, it increases for DODAB, also decreasing its lateral viscosity. Infrared spectroscopy shows that the interaction of Eu-5-SO3-HQ alters the ordering of the lipids' alkyl chains, impacting the monolayer's molecular packing. These results show that the interaction of Eu-5SO3-HQ with lipid monolayers at the air-water is modulated by the composition of the polar head, which can be supportive in the preparation of nanodevices for molecular probing.

Assuntos

RESUMO

BACKGROUND: The main functions of the endotracheal tube (ETT) cuff are to prevent aspiration and to allow pressurization of the respiratory system. For this purpose, it is essential to maintain adequate pressure inside the cuff, thus reducing the risks for the patient. It is regularly checked using a manometer and is considered the best alternative. The objective of this study was to evaluate the cuff pressure behavior of different ETTs during the simulation of an inflation maneuver using different manometers. METHODS: A bench study was performed. Four brands of 8-mm internal diameter single lumen with a Murphy eye ETT with cuff and 3 different brands of manometers were used. In addition, a pulmonary mechanics monitor was connected to the inside of the cuff through the body of the distal end of the ETT. RESULTS: A total of 528 measurements were made on the 4 ETTs. During the complete procedure (connection and disconnection), there was a significant pressure drop of 7 ± 1.4 cm H2O from the initial pressure (Pinitial) (P < .001), of which 6 ± 1.4 cm H2O was lost during connection (difference between Pinitial and Pconnection). The Preconnection value was 19.1 ± 1.6 cm H2O, showing a significant total pressure drop of 11 ± 1.6 cm H2O (difference between Pinitial and Preconnection) (P < .001). The Pfinal mean was 29.6 ± 1.3 cm H2O. Significant differences were found between manometers according to the time of measurement. A similar phenomenon was evidenced when analyzing different ETTs. CONCLUSIONS: Significant pressure changes occur secondary to ETT cuff measurement, which has important implications for patient safety.

Assuntos

RESUMO

An Open Brain-Computer Interface (OpenBCI) provides unparalleled freedom and flexibility through open-source hardware and firmware at a low-cost implementation. It exploits robust hardware platforms and powerful software development kits to create customized drivers with advanced capabilities. Still, several restrictions may significantly reduce the performance of OpenBCI. These limitations include the need for more effective communication between computers and peripheral devices and more flexibility for fast settings under specific protocols for neurophysiological data. This paper describes a flexible and scalable OpenBCI framework for electroencephalographic (EEG) data experiments using the Cyton acquisition board with updated drivers to maximize the hardware benefits of ADS1299 platforms. The framework handles distributed computing tasks and supports multiple sampling rates, communication protocols, free electrode placement, and single marker synchronization. As a result, the OpenBCI system delivers real-time feedback and controlled execution of EEG-based clinical protocols for implementing the steps of neural recording, decoding, stimulation, and real-time analysis. In addition, the system incorporates automatic background configuration and user-friendly widgets for stimuli delivery. Motor imagery tests the closed-loop BCI designed to enable real-time streaming within the required latency and jitter ranges. Therefore, the presented framework offers a promising solution for tailored neurophysiological data processing.

Assuntos

RESUMO

The human sensory receptors are morphologically specialized to transduce specific stimuli into the brain. However, when an injury occurs, mainly in the spinal cord, which can be of traumatic or non-traumatic origin, it provokes various degrees of sensory deficits, autonomic, motor and sphincter dysfunction below the level of the injury. Based on this, a new therapeutic modality is being proposed by neuroscientist Miguel Nicolelis, which is based on the brain-machine interface, that is, using other pathways so that the information can reach the cerebral cortex and thus be consciously processed (AU).

Os receptores sensoriais humanos são morfologicamente especializados para realizar a transdução de estímulos específicos para o encéfalo. Entretanto, quando ocorre uma lesão, principalmente, na medula espinal, que pode ser de origem traumática e não traumática, provocam diversos graus de déficits sensoriais, disfunção autônoma, motora e esfincteriana, abaixo do nível da lesão. Com base nisso, uma nova modalidade terapêutica está sendo proposto pelo neurocientista Miguel Nicolelis, que tem como base a interface cérebro máquina, isto é, utilizar-se de outras vias para que as informações possam chegar no córtex cerebral e assim serem processadas conscientemente.Palavras-chave: Interfaces cérebro-computador, Neurociências, Órgãos dos sentidos (AU).

Assuntos

RESUMO

This review summarizes the relevant developments in preparing wrinkled structures with variable characteristics. These include the formation of smart interfaces with reversible wrinkle formation, the construction of wrinkles in non-planar supports, or, more interestingly, the development of complex hierarchically structured wrinkled patterns. Smart wrinkled surfaces obtained using light-responsive, pH-responsive, temperature-responsive, and electromagnetic-responsive polymers are thoroughly described. These systems control the formation of wrinkles in particular surface positions and the reversible construction of planar-wrinkled surfaces. This know-how of non-planar substrates has been recently extended to other structures, thus forming wrinkled patterns on solid, hollow spheres, cylinders, and cylindrical tubes. Finally, this bibliographic analysis also presents some illustrative examples of the potential of wrinkle formation to create more complex patterns, including gradient structures and hierarchically multiscale-ordered wrinkles. The orientation and the wrinkle characteristics (amplitude and period) can also be modulated according to the requested application.

RESUMO

Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti6 O12 cluster, doping Al in Si11 (4Al@Si11 ) and Na10 supported on graphene (Na10 @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo8 C4 cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.

RESUMO

Continuous positive airway pressure is the gold standard treatment for obstructive sleep apnea. Different interfaces with distinct characteristics, advantages, and disadvantages are available, which may influence long-term adherence. Oronasal masks have been increasingly used. However, recent evidence suggest that nasal masks are more effective when continuous positive airway pressure is used to treat obstructive sleep apnea. The main objective of this review is to describe the basis for the selection of the interface for the treatment of obstructive sleep apnea with continuous positive airway pressure.

Assuntos

RESUMO

Herein we have proposed that a redox mechanism can produce surface charges and negative zeta potential on an aqueous graphite dispersion. Graphite was kept in contact with a concentrated ammonia aqueous solution, washed, and exfoliated in water, resulting in a dispersion with lyophobic nature. Ammonia treatment did not provide functional groups or nitrogen doping to graphite. Moreover, this material was washed twice before sonication to remove most hydroxide. Therefore, neither functional groups, nitrogen atoms, nor hydroxide excess is responsible for the zeta potential. Kelvin probe force microscopy has shown that the ammonia-treated and exfoliated graphite has higher Fermi level than the water-treated material, indicating that the contact between ammonia and graphite promotes redox reactions that provide electrons to graphite. These electrons raise the Fermi level of graphite and generate the negative zeta potential, consequently, they account for the colloidal stability.

RESUMO

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the "happiness emotional brain state" of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4-1 Median = 6.563%; Range = 4.10-27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1-15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range -1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.

RESUMO

Industry 4.0 involves various areas of engineering such as advanced robotics, Internet of Things, simulation, and augmented reality, which are focused on the development of smart factories. The present work presents the design and application of the methodology for the development of augmented reality applications (MeDARA) using a concrete, pictorial, and abstract approach with the intention of promoting the knowledge, skills, and attitudes of the students within the conceptual framework of educational mechatronics (EMCF). The flight of a drone is presented as a case study, where the concrete level involves the manipulation of the drone in a simulation; the graphic level requires the elaboration of an experiential storyboard that shows the scenes of the student's interaction with the drone in the concrete level; and finally, the abstract level involves the planning of user stories and acceptance criteria, the computer design of the drone, the mock-ups of the application, the coding in Unity and Android Studio, and its integration to perform unit and acceptance tests. Finally, evidence of the tests is shown to demonstrate the results of the application of the MeDARA.

Assuntos

RESUMO

Technologies for self-care can drive participatory health and promote independence of older adults. One self-care activity is regularly measuring and registering personal health indicators (self-reporting). Older adults may benefit from this practice, as they are more likely to have chronic health issues and have specific self-monitoring needs. However, self-reporting technologies are usually not designed specifically for them. Pain is usually measured using patient reports compiled during medical appointments, although this process may be affected by memory bias and under reporting of fluctuating pain. To address these issues, we introduced a simple tangible interface to self-report pain levels and conducted a three-hour evaluation with 24 older adults. The goal of this study was to identify whether specific activities, activity levels or pain levels trigger older adults to self-report their pain level, besides to understand how older adults would use such a device. Within the limited time frame of the experiment, the majority of our participants chose to report pain when they felt it most, not reporting lower levels of pain. No evidence was found to suggest a relationship between the reporting of pain and the activity (or activity level). Several design insights intended to improve the design of technologies are provided.

Assuntos

RESUMO

The data consist of electroencephalography (EEG) signals acquired by means of low-cost consumer-grade devices from 10 participants (four females, right-handed, mean age ± SD = 26.1 ± 4.0 years) without any previous experience in Brain-Computer Interfaces (BCIs) usage. The BCI protocol consisted of two conditions, namely the kinesthetic imagination of grasping movement (motor imagery, MI) of the dominant hand and a rest/idle condition. Five protocol runs were required to be performed by each participant in a single-day session, of about 1.5 h. The first run, called RUN0, involved 5 trials of real grasping movement together with the same number of trials in a rest condition. This first run was done to both better explain the protocol and to encourage the participant to focus on the sensation of executing the movement. The rest of the runs (RUN1-RUN4) were identical, consisting of 20 trials for each condition presented in a random order. The electrical brain activity was registered from 15 electrodes covering the sensorimotor area, at a sampling frequency of 125 Hz. Muscle activity of the dominant hand was controlled via the electromyography (EMG) activity by two electrodes placed at two antagonist muscles involved in the flexion/extension of the wrist. The recordings were performed in a non-shielded office, by means of low-cost consumer grade devices and free multi-platform open source software. The EMG corruption level was analyzed and EEG trials for which the EMG activity was higher than a prescribed threshold value, were discarded. During acquisition, EEG data was digitally band-pass filtered between 0.5 and 45 Hz. These data provide a motor imagery vs. rest EEG dataset, relevant for BCI for motor rehabilitation applications. Since the recordings were performed by means of low-cost consumer grade devices in a non-controlled environment, this dataset provides an excellent source for exploring robust brain decoding techniques for future in-home BCI usage.

RESUMO

Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

Assuntos