Influence of Temporal and Frequency Selective Patterns Combined with CSP Layers on Performance in Exoskeleton-Assisted Motor Imagery Tasks.

Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain-computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs.

Continuous reach-to-grasp motion recognition based on an extreme learning machine algorithm using sEMG signals.

Recognizing user intention in reach-to-grasp motions is a critical challenge in rehabilitation engineering. To address this, a Machine Learning (ML) algorithm based on the Extreme Learning Machine (ELM) was developed for identifying motor actions using surface Electromyography (sEMG) during continuous reach-to-grasp movements, involving multiple Degrees of Freedom (DoFs). This study explores feature extraction methods based on time domain and autoregressive models to evaluate ELM performance under different conditions.

Gait Training-Based Motor Imagery and EEG Neurofeedback in Lokomat: A Clinical Intervention With Complete Spinal Cord Injury Individuals.

Robotic systems, such as Lokomat® have shown promising results in people with severe motor impairments, who suffered a stroke or other neurological damage. Robotic devices have also been used by people with more challenging damages, such as Spinal Cord Injury (SCI), using feedback strategies that provide information about the brain activity in real-time. This study proposes a novel Motor Imagery (MI)-based Electroencephalogram (EEG) Visual Neurofeedback (VNFB) system for Lokomat® to teach individuals how to modulate their own μ (8-12 Hz) and β (15-20 Hz) rhythms during passive walking.

Unraveling Transformative Effects after tDCS and BCI Intervention in Chronic Post-Stroke Patient Rehabilitation-An Alternative Treatment Design Study.

Stroke is a debilitating clinical condition resulting from a brain infarction or hemorrhage that poses significant challenges for motor function restoration. Previous studies have shown the potential of applying transcranial direct current stimulation (tDCS) to improve neuroplasticity in patients with neurological diseases or disorders. By modulating the cortical excitability, tDCS can enhance the effects of conventional therapies.

Coupling Effects of Cross-Corticomuscular Association during Object Manipulation Tasks on Different Haptic Sensations.

The effects of corticomuscular connectivity during object manipulation tasks with different haptic sensations have not been quantitatively investigated. Connectivity analyses enable the study of cortical effects and muscle responses during movements, revealing communication pathways between the brain and muscles. This study aims to examine the corticomuscular connectivity of three Electroencephalography (EEG) channels and five muscles during object manipulation tasks involving contact surfaces of Sandpaper, Suede, and Silk.

EEG motor imagery classification using deep learning approaches in naïve BCI users.

Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users.

Lower-limb kinematic reconstruction during pedaling tasks from EEG signals using Unscented Kalman filter.

Kinematic reconstruction of lower-limb movements using electroencephalography (EEG) has been used in several rehabilitation systems. However, the nonlinear relationship between neural activity and limb movement may challenge decoders in real-time Brain-Computer Interface (BCI) applications. This paper proposes a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower-limb kinematics from EEG signals during pedaling.

A deep learning approach to estimate pulse rate by remote photoplethysmography.

This study proposes a U-net shaped Deep Neural Network (DNN) model to extract remote photoplethysmography (rPPG) signals from skin color signals to estimate Pulse Rate (PR).Three input window sizes are used in the DNN: 256 samples (5.12 s), 512 samples (10.

Molecular dynamics of the COVID-19 pandemic in Espirito Santo (Brazil) and border States.

This study represents the first overview of the epidemiological dynamics of SARS-CoV-2 in Espirito Santo (ES) State, Brazil, filling in knowledge on this topic, observing data collected in the State, and aiming at understanding the epidemiological dynamics of the virus in ES, as well as its possible routes of transmission and dissemination. . Our results highlight that, so far, nine lineages have been identified with ES State.

Usability, occupational performance and satisfaction evaluation of a smart environment controlled by infrared oculography by people with severe motor disabilities.

A smart environment is an assistive technology space that can enable people with motor disabilities to control their equipment (TV, radio, fan, etc.) through a human-machine interface activated by different inputs. However, assistive technology resources are not always considered useful, reaching quite high abandonment rate.

Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field.

Background And Objective: Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time.

A SSVEP-BCI Setup Based on Depth-of-Field.

In optical systems, the range of distance near the point of focus where objects are perceived sharply is referred as depth-of-field; objects outside this region are defocused and blurred. Furthermore, ophthalmology studies state that the amplitude and the latency of visual evoked potentials are affected by defocusing. In this context, this paper evaluates a novel setup for a steady-state visual evoked potential (SSVEP) brain-computer interface, in which two stimuli are presented together in the center of the user's field of view but at different distances ensuring that if one stimulus is focused on, the other one is non-focused, and vice versa.

An intelligent space for mobile robot localization using a multi-camera system.

This paper describes an intelligent space, whose objective is to localize and control robots or robotic wheelchairs to help people. Such an intelligent space has 11 cameras distributed in two laboratories and a corridor. The cameras are fixed in the environment, and image capturing is done synchronously.

Towards a new modality-independent interface for a robotic wheelchair.

This work presents the development of a robotic wheelchair that can be commanded by users in a supervised way or by a fully automatic unsupervised navigation system. It provides flexibility to choose different modalities to command the wheelchair, in addition to be suitable for people with different levels of disabilities. Users can command the wheelchair based on their eye blinks, eye movements, head movements, by sip-and-puff and through brain signals.

Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface.

This work presents a brain-computer interface (BCI) used to operate a robotic wheelchair. The experiments were performed on 15 subjects (13 of them healthy). The BCI is based on steady-state visual-evoked potentials (SSVEP) and the stimuli flickering are performed at high frequency (37, 38, 39 and 40 Hz).

SSVEP-BCI implementation for 37-40 Hz frequency range.

This work presents a Brain-Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), using higher stimulus frequencies (>30 Hz). Using a statistical test and a decision tree, the real-time EEG registers of six volunteers are analyzed, with the classification result updated each second. The BCI developed does not need any kind of settings or adjustments, which makes it more general.

Autonomous assistance navigation for robotic wheelchairs in confined spaces.

In this work, a visual interface for the assistance of a robotic wheelchair's navigation is presented. The visual interface is developed for the navigation in confined spaces such as narrows corridors or corridor-ends. The interface performs two navigation modus: non-autonomous and autonomous.

Incremental HMM training applied to ECG signal analysis.

This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual.

Human-machine interfaces based on EMG and EEG applied to robotic systems.

Background: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions.