Study of the Functional Brain Connectivity and Lower-Limb Motor Imagery Performance After Transcranial Direct Current Stimulation

The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on electroencephalography is simulated in offline analysis to study the effect that tDCS has over different electrode configurations for the BCI. This way, the BCI performance is used as a validation index of the effect of the tDCS setup by the analysis of the classifier accuracy of the experimental sessions. In addition, the relationship between the brain connectivity and the BCI accuracy performance is analyzed. Results indicate that tDCS group, in comparison to the placebo sham group, shows a higher significant number of connectivity interactions in the motor electrodes during MI tasks and an increasing BCI accuracy over the days. However, the asymmetric tDCS setup does not improve the BCI performance of the electrodes in the intended hemisphereThis research has been carried out in the framework of the project Walk — Controlling lower-limb exoskeletons by means of BMIs to assist people with walking disabilities (RTI2018-096677-B-I00Funded by the Spanish Ministry of Science and Innovation, the Spanish State Agency of Research and the European Union through the European Regional Development Fund;by the Consellería de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana) and the European Social Fund in the framework of the project ‘Desarrollo de nuevas interfaces cerebro-m´aquina para la rehabilitaci`on de miembro inferior’ (GV/2019/009).Also, the Mexican Council of Science and Technology (CONACyT) provided J. A. Gaxiola-Tirado his scholarshi

Assistance Robotics and Biosensors

This Special Issue is focused on breakthrough developments in the field of biosensors and current scientific progress in biomedical signal processing. The papers address innovative solutions in assistance robotics based on bioelectrical signals, including: Affordable biosensor technology, affordable assistive-robotics devices, new techniques in myoelectric control and advances in brain–machine interfacing

Improving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an Exoskeleton

The aim of this work was to test if a novel transcranial direct current stimulation (tDCS) montage boosts the accuracy of lower limb motor imagery (MI) detection by using a real-time brain-machine interface (BMI) based on electroencephalographic (EEG) signals. The tDCS montage designed was composed of two anodes and one cathode: one anode over the right cerebrocerebellum, the other over the motor cortex in Cz, and the cathode over FC2 (using the International 10–10 system). The BMI was designed to detect two MI states: relax and gait MI; and was based on finding the power at the frequency which attained the maximum power difference between the two mental states at each selected EEG electrode. Two different single-blind experiments were conducted, E1 and a pilot test E2. E1 was based on visual cues and feedback and E2 was based on auditory cues and a lower limb exoskeleton as feedback. Twelve subjects participated in E1, while four did so in E2. For both experiments, subjects were separated into two equally-sized groups: sham and active tDCS. The active tDCS group achieved 12.6 and 8.2% higher detection accuracy than the sham group in E1 and E2, respectively, reaching 65 and 81.6% mean detection accuracy in each experiment. The limited results suggest that the exoskeleton (E2) enhanced the detection of the MI tasks with respect to the visual feedback (E1), increasing the accuracy obtained in 16.7 and 21.2% for the active tDCS and sham groups, respectively. Thus, the small pilot study E2 indicates that using an exoskeleton in real-time has the potential of improving the rehabilitation process of cerebrovascular accident (CVA) patients, but larger studies are needed in order to further confirm this claim

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)

Effective EEG analysis for advanced AI-driven motor imagery BCI systems

Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets.Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets

Manipulador aéreo con brazos antropomórficos de articulaciones flexibles

[Resumen] Este artículo presenta el primer robot manipulador aéreo con dos brazos antropomórficos diseñado para aplicarse en tareas de inspección y mantenimiento en entornos industriales de difícil acceso para operarios humanos. El robot consiste en una plataforma aérea multirrotor equipada con dos brazos antropomórficos ultraligeros, así como el sistema de control integrado de la plataforma y los brazos. Una de las principales características del manipulador es la flexibilidad mecánica proporcionada en todas las articulaciones, lo que aumenta la seguridad en las interacciones físicas con el entorno y la protección del propio robot. Para ello se ha introducido un compacto y simple mecanismo de transmisión por muelle entre el eje del servo y el enlace de salida. La estructura en aluminio de los brazos ha sido cuidadosamente diseñada de forma que los actuadores estén aislados frente a cargas radiales y axiales que los puedan dañar. El manipulador desarrollado ha sido validado a través de experimentos en base fija y en pruebas de vuelo en exteriores.Ministerio de Economía y Competitividad; DPI2014-5983-C2-1-

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conferenc