Denis Delisle-Rodriguez | Universidad de Oriente, Santiago de Cuba (original) (raw)

Adaptive Spatial Filter Based on Similarity Indice by Denis Delisle-Rodriguez

An active orthosis (AO) is a robotic device that assists both human gait and rehabilitation thera... more An active orthosis (AO) is a robotic device that assists both human gait and rehabilitation therapy. This work proposes portable AOs, one for the knee joint and another for the ankle joint. Both AOs will be used to complete a robotic system that improves gait rehabilitation. The requirements for actuator selection, the biomechanical considerations during the AO design, the finite element method, and a control approach based on electroencepha-lographic and surface electromyographic signals are reviewed. This work contributes to the design of AOs for users with foot drop and knee flexion impairment. However, the potential of the proposed AOs to be part of a robotic gait rehabilitation system that improves the quality of life of stroke survivors requires further investigation.

⎯ A system for simultaneous register of the photoplethysmographic and electrocardiographic signal... more ⎯ A system for simultaneous register of the photoplethysmographic and electrocardiographic signal in a patient has been characterized and designed. The system is compound by three fitting-out channels and a common module for digitalization and data transmission to a PC for storage and analysis (real time or not) .The results of this system about filters frequency, slew rate values and top saturation entry tensions are adequate for individual analysis and match theory. The system designed make possible to study the influence of the autonomic nervous system over cardiovascular and circulatory system.

ABSTRACT This work proposes the development of an electroencephalography (EEG) and surface electr... more ABSTRACT This work proposes the development of an electroencephalography (EEG) and surface electromyography (sEMG) wireless system for a robotic walker. The goal is to validate its performance at the front-end channels through steady state visually evoked potential (SSVEP) and events-related desynchronization/ synchronization (ERD/ERS). The frequencies employed in the SSVEP are captured on the occipital region through FP1-O1 EEG electrodes. Furthermore, ERD/ERS pattern are obtained on the cortical motor areas through C3-FZ and C4-FZ electrodes during button movements with index finger of the right hand. In addition, the myoelectric activity (sEMG) of the peroneus longus muscle is obtained during ankle flexion-extensions. The front-end designed can be used to obtain motor patterns from the brain and muscle activities.

2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI), 2012

Physiological Measurement, 2013

This work presents the evaluation of pulse rate variability (PRV) obtained from pulse onsets of p... more This work presents the evaluation of pulse rate variability (PRV) obtained from pulse onsets of photoplethysmographic (PPG) signals. Three published algorithms were used to determine the pulse onsets: diastolic point, maximum second derivative and tangent intersection. Temporal series of pulse onsets were obtained for each method, and several variability indices were derived from these series. Simultaneous ECG and PPG records were acquired from 37 healthy volunteers to evaluate the interchangeability between PRV indices and heart rate variability (HRV) indices by the Bland-Altman method. Furthermore, the concordance correlation coefficient was used to correlate the indices. In all the cases, PRV indices obtained through the tangent intersection method showed better accuracy and precision (Bland-Altman analysis, bias ± 1.96 standard deviation: low frequency, LF ms 2 = −28.06 ± 72.68; high frequency, HF ms 2 = −68.23 ± 192.85; high frequency in normalized units, HF nu = −2.02 ± 7.08; LF/HF = 0.17 ± 0.71) and higher correlation (concordance correlation coefficients: low frequency, LF ms 2 = 0.99; high frequency, HF ms 2 = 0.98; high frequency in normalized units, HF nu = 0.97; LF/HF = 0.90) with HRV indices than other methods, and could be used as a good surrogate of HRV. H F Posada-Quintero et al Allen J 2007 Photoplethysmography and its application in clinical physiological measurement Physiol. Meas. 28 R1-39 Appel M L, Berger R D, Saul J P, Smith J M and Cohen R J 1989 Beat to beat variability in cardiovascular variables: noise or music? J. Am. Coll. Cardiol. 14 1139-48 Asmar R 1999 Arterial Stiffness and Pulse Wave Velocity. Clinical Applications (Amsterdam: Elsevier) Bigger J T Jr, Fleiss J L, Rolnitzky L M, Steinman R C and Schneider WJ 1991 Time course of recovery of heart period variability after myocardial infarction J. Am. Coll. Cardiol. 18 1643-9 Bistra N and Ivo I 2010 An automated algorithm for fast pulse wave detection Int. J. BIOautomation 14 203-16 Bland J M and Altman D G 1986 Statistical methods for assessing agreement between two methods of clinical measurement Lancet 1 307-10 Challoner A V J 1979 Photoelectric plethysmography for estimating cutaneous blood flow Non-Invasive Physiological Measurements vol 1 ed P Rolfe (London: Academic) pp 125-51 Charlot K, Cornolo J, Brugniaux J V, Richalet J P and Pichon A 2009 Interchangeability between heart rate and photoplethysmography variabilities during sympathetic stimulations Physiol. Meas. 30 1357-69 Chiu Y C, Arand P W, Shroff S G, Feldman T and Carroll J D 1991 Determination of pulse wave velocities with computerized algorithms Am. Heart J. 121 1460-70 Cohn J N, Finkelstein S, McVeigh G, Morgan D, LeMay L, Robinson J and Mock J 1995 Noninvasive pulse wave analysis for the early detection of vascular disease Hypertension 26 503-8 Cuadra M B, Esteban R, Delisle D, Mántaras M C, Perrone M S, Fainstein D, Zapata D, Vázquez C and Nicolo L 2008 Sistema para el registro simultáneo de la onda de pulso y el electrocardiograma orientado al estudio de la regulación autonómica 4th Int. Conf. FIE'08 Davies J I and Struthers A D 2004 Beyond blood pressure: pulse wave analysis a better way of assessing cardiovascular risk? Future Cardiol. 1 69-78 de Sá Ferreira A, Filho J B, Cordovil I and de Souza M N 2009 Three section transmission line arterial model for noninvasive assessment of vascular remodelingin primary hypertension Biomed. Signal Process. Control 4 2-6 Eckberg D L 1997 Sympathovagal balance: a critical appraisal Circulation 96 3224-32 Egidijus K R G and Arunas V 2005 Mathematical methods for determining the foot point of the arterial pulse wave and evaluation of proposed methods Inform. Technol. Control 34 29-36 Gil E, Orini M, Bailón R, Vergara J M, Mainardi L and Laguna P 2010 Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions Physiol. Meas. 31 1271-90 Hayano J, Barros A K, Kamiya A, Ohte N and Yasuma F 2005 Assessment of pulse rate variability by the method of pulse frequency demodulation Biomed. Eng. Online 4 62 Hu X, Xu P, Lee D, Vespa P and Bergsneider M 2008 An algorithm of extracting intracranial pressure latency relative to electrocardiogram R wave Physiol. Meas. 29 459-71 Huikuri H V, Koistinen M J, Yli-Mayry S, Airaksinen K E, Seppanen T, Ikaheimo M J and Myerburg R J 1995 Impaired low-frequency oscillations of heart rate in patients with prior acute myocardial infarction and lifethreatening arrhythmias Am. J. Cardiol. 76 56-60 Kleiger R E, Miller J P, Bigger J T Jr and Moss A J 1987 Decreased heart rate variability and its association with increased mortality after acute myocardial infarction Am. J. Cardiol. 59 256-62 Lin L I 1989 A concordance correlation coefficient to evaluate reproducibility Biometrics 45 255-68 Lu G and Yang F 2009 Limitations of oximetry to measure heart rate variability measures Cardiovasc. Eng. 9 119-25 Lu G, Yang F, Taylor J A and Stein J F 2009 A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects J. Med. Eng. Technol. 33 634-41 Lu S, Zhao H, Ju K, Shin K, Lee M, Shelley K and Chon K 2008 Can photoplethysmography variability serve as an alternative approach to obtain heart rate variability information? J. Clin. Monit. Comput. 22 23-9 Martin W A, Camenzind E and Burkhard P R 2003 ECG artifact due to deep brain stimulation Lancet 361 1431 Martinez J P, Almeida R, Olmos S, Rocha A P and Laguna P 2004 A wavelet-based ECG delineator: evaluation on standard databases IEEE Trans. Biomed. Eng. 51 570-81 Mitchell G F, Pfeffer M A, Finn P V and Pfeffer J M 1997 Comparison of techniques for measuring pulse-wave velocity in the rat J. Appl. Physiol. 82 203-10 Nunan D, Donovan G, Jakovljevic D G, Hodges L D, Sandercock G R and Brodie D A 2009 Validity and reliability of short-term heart-rate variability from the Polar S810 Med. Sci. Sports Exerc. 41 243-50 O'Rourke M F and Gallagher D E 1996 Pulse wave analysis J. Hypertens. 14 (Suppl. 5) S147-57 O'Rourke M F 1999 Isolated systolic hypertension, pulse pressure, and arterial stiffness as risk factors for cardiovascular disease Curr. Hypertens. Rep. 1 204-11 O'Rourke M F, Aviolo A P and Kelly R P 1992 The Arterial Pulse (Baltimore, MA: Lea and Febiger) Pomeranz B et al 1985 Assessment of autonomic function in humans by heart rate spectral analysis Am. J. Physiol. 248 H151-3

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

The selection of features is generally the most difficult field to model in BCIs. Therefore, time... more The selection of features is generally the most difficult field to model in BCIs. Therefore, time and effort are invested in individual feature selection prior to data set training. Another great difficulty regarding the model of the BCI topology is the brain signal variability between users. How should this topology be in order to implement a system that can be used by large number of users with an optimal set of features? The proposal presented in this paper allows for obtaining feature reduction and classifier selection based on software agents. The software agents contain Genetic Algorithms (GA) and a cost function. GA used entropy and mutual information to choose the number of features. For the classifier selection a cost function was defined. Success rate and Cohen's Kappa coefficient are used as parameters to evaluate the classifiers performance. The obtained results allow finding a topology represented as a neural model for an adaptive BCI, where the number of the channels, features and the classifier are interrelated. The minimal subset of features and the optimal classifier were obtained with the adaptive BCI. Only three EEG channels were needed to obtain a success rate of 93% for the BCI competition III data set IVa.

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 2014

This work presents an alternative method to detect events correlated to eyes opening and closing,... more This work presents an alternative method to detect events correlated to eyes opening and closing, based on electroencephalography (EEG) measured from the occipital lobe. The goal is to propose a method based on linear discriminant function to classify segments of EEG signals that contain activities originated by eyes closing. A linear discriminant function presented by Fisher is employed to detect these activities on segments of 2s. This method showed a good values of sensitivity (SE ≥ 85 %) and specificity (SP ≥ 60 %). This approach can be used to control the switching of a brain computer interface (BCI).

Papers by Denis Delisle-Rodriguez

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018

This work presents a brain-computer interface (BCI) based on unsupervised methods for conveying c... more This work presents a brain-computer interface (BCI) based on unsupervised methods for conveying control commands to a robotic exoskeleton, in order to provide support to patients with severe motor disability during walking. For this purpose, an adaptive spatial filter based on similarity indices is proposed to preserve the useful information on electroencephalography (EEG) signals. Additionally, a method for feature selection based on the Maximal Information Compression Index (MICI), and the representation entropy (RE) is used, increasing its robustness for uncertain patterns, such as gait planning. Good values of accuracy (ACC > 75%) and false positive rate (FPR< 10%) were obtained for four subjects. Thus, this BCI based on unsupervised method may be suitable to recognize uncertainty pattern, such as gait planning.

This work presents a user interface, which may operate a robotic upper-limb exoskeleton for rehab... more This work presents a user interface, which may operate a robotic upper-limb exoskeleton for rehabilitation of people with motor disability. The interface developed can be used to program several routines of movements, which may be executed through a robotic exoskeleton, storing in a database the personal and clinical information obtained on each patient during the rehabilitation process. The interface includes an electromyographic biofeedback module that can display to patients their muscular activation during the motor activity. The results showed that the user interface may control a robotic exoskeleton for upper limbs, which is useful to increase the patient motivation through the biofeedback information, allowing the tracking of the patient´s progress.

Sensors

The development of Brain–Computer Interfaces based on Motor Imagery (MI) tasks is a relevant rese... more The development of Brain–Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.

Sensors

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, wh... more COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level—SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The I...

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

A prominent change is being carried out in the fields of rehabilitation and assistive exoskeleton... more A prominent change is being carried out in the fields of rehabilitation and assistive exoskeletons in order to actively aid or restore legged locomotion for individuals suffering from muscular impairments, muscle weakness, neurologic injury, or disabilities that affect the lower limbs. This paper presents a characterization of knee motion patterns from Surface Electromyography (sEMG) signals, measured in the Erector spinae (ES) muscle. Feature extraction (mean absolute value, waveform length and auto-regressive model) and pattern classification methods (Linear Discrimination Analysis, K-Nearest Neighborhood and Support Vector Machine) are applied for recognition of eight-movement classes. Additionally, several channels setup are analyzed to obtain a suitable electrodes array. The results were evaluated based on signals measured from lower limb using quantitative metric such as error rate, sensitivity, specificity and predictive positive value. A high accuracy (&amp;gt; 95%) was obtained, which suggest that it is possible to detect the knee motion intention from ES muscle, as well as to reduce the electrode number (from 2 to 3 channels) to obtain an optimal electrodes array. This implementation can be applied for myoelectric control of lower limb active exoskeletons.

IEEE Transactions on Instrumentation and Measurement

In this paper, we aim at finding the smallest set of EEG channels that can ensure highly accurate... more In this paper, we aim at finding the smallest set of EEG channels that can ensure highly accurate classification of motor imagery (MI) dataset and maintain the optimum Kappa score. Non-negative matrix factorization (NMF) is used for important and discriminant EEG channel selection. Further, the theory of Riemannian geometry in the manifold of covariance matrices is used for feature extraction. At last, the neighborhood component feature selection (NCFS) algorithm is used to select the small subset of important features from the given set of features. The significance of the proposed work is two-fold: 1) it greatly reduces the time complexity and the amount of overfitting by reducing the unnecessary EEG channels and redundant features. 2) it increases the classification accuracy of the model by selecting only subject-specific EEG channels. The proposed algorithm is tested on BCI Competition IV,2a dataset to validate the performance. The proposed approach has achieved 77.91% average c...

Sensors, 2021

Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative... more Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced an...

IEEE Access, 2021

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for reha... more Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user's motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications.

Sensors, 2017

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective o... more Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users' anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.

2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI), 2012

Physiological Measurement, 2013

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 2014

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018

Sensors

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

IEEE Transactions on Instrumentation and Measurement

Sensors, 2021

IEEE Access, 2021

Sensors, 2017

Procedia Manufacturing, 2015

The integration of lower limb exoskeletons with robotic walkers allows obtaining a system to impr... more The integration of lower limb exoskeletons with robotic walkers allows obtaining a system to improve mobility and security during gait rehabilitation. In this work, the evaluation of human motion intention (HMI) based on electroencephalogram (EEG) and surface electromyography (sEMG) signals are analyzed for a knee exoskeleton control as a preliminary study for gait neurorehabilitation with a hybrid robotic system. This system consists of the knee exoskeleton H2 and the UFES's Smart Walker, which are used to restore the neuromotor control function of subjects with neural injuries. An experimental protocol was developed to identify patterns to control the exoskeleton in accordance with the HMI-based on EEG/sEMG. The EEG and sEMGsignalsare recorded during thefollowing activities: stand-up/sit-down and knee flexion/extension. HMI is analyzed through both event-related desynchronization/synchronization (ERD/ERS) and slow cortical potential, as well as the myoelectric patternclassification related to lower limb. The feature extraction from sEMG signals is based on vector combinations in time and frequency domain which are used for a pattern classification stage trough an artificial neural network with LevenbergMarquadt training algorithm and support vector machine. Preliminary results shown that a combination of EEG/sEMG signals can be used to define a control strategy for the robotic system.

Frontiers of Mechanical Engineering, 2015

ABSTRACT An active orthosis (AO) is a robotic device that assists both human gait and rehabilitat... more ABSTRACT An active orthosis (AO) is a robotic device that assists both human gait and rehabilitation therapy. This work proposes portable AOs, one for the knee joint and another for the ankle joint. Both AOs will be used to complete a robotic system that improves gait rehabilitation. The requirements for actuator selection, the biomechanical considerations during the AO design, the finite element method, and a control approach based on electroencephalographic and surface electromyographic signals are reviewed. This work contributes to the design of AOs for users with foot drop and knee flexion impairment. However, the potential of the proposed AOs to be part of a robotic gait rehabilitation system that improves the quality of life of stroke survivors requires further investigation.

International Journal of Bioscience, Biochemistry and Bioinformatics, 2013

Traditional methodologies use electrocardiographic (ECG) signals to develop automatic methods for... more Traditional methodologies use electrocardiographic (ECG) signals to develop automatic methods for onset and peak detection on the arterial pulse wave. An alternative method using pattern recognition is implemented to detect onset and peak fiducial points, using Self Organizing Maps (SOM). In the present work SOM neural networks were trained with a dataset of signals with information about localization of onset and peak points. Later on, the trained network was used to make the detection on a validation dataset. This was developed using a shifting temporal windowing, which is presented to the network to decide whether the window corresponds to an onset or peak in the pulse wave. Results of the classification reach 97.93% over the validation dataset. Sensitivity and positive predictivity measures were used to assess the proposed method, reaching 100% for sensitivity and 99.84% for the positive predictivity detecting peaks in the signals. This proposal takes advantages from SOM neural networks for pattern classification and detection. Additionally, ECG signal is not necessary in the presented methodology.

Sensors (Basel, Switzerland), Jan 25, 2017

This work presents a new on-line adaptive filter, which is based on a similarity analysis between... more This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to ...

Research on Biomedical Engineering

— Currently, cerebrovascular diseases are one of the main health problems. Part of the patient’s ... more — Currently, cerebrovascular diseases are one of the main health problems. Part of the patient’s rehabilitation process, affected by this disease, is manually performed by a physiotherapist, which, due to physical exhaustion, could affect the performance of patient recovery. In this paper is proposed a robotic exoskeleton for upper limb rehabilitation, which enables assist or supports the therapist’s work. In the first stage, the exoskeleton is controlled passively through programmed commands and routines. Later, a second stage is proposed for biofeedback control system using the exoskeleton and signals acquired through bioinstrumentation equipment. This system will allows the acquisition of the surface electromyography signals (sEMG), as well as proprioceptive information for signal processing and movement’s intention detection of upper limb. As results, are presented the

XXVI Brazilian Congress on Biomedical Engineering, 2019

A recent study in the literature showed that eight paraplegic patients with chronic spinal cord i... more A recent study in the literature showed that eight paraplegic patients with chronic spinal cord injury, who underwent 12 months of training in brain-machine interface (BMI), based on neurorehabilitation using a virtual system and a very high cost exoskeleton, experienced neurological enhancements in somatic sensation, as well as motor improvements. A possible low-cost solution is to use a robotic monocycle instead of an exoskeleton, since the exercise of pedaling a monocycle has the potential to provide a high number of flexion and extension repetitions of the lower limb in reasonable therapeutic time periods. The objective of this work is to develop a neurorehabilitation platform based on electroencephalography (EEG), surface electromyography (sEMG) and immersive virtual reality (IVR), and using a robotic monocycle to move the user’s legs. The monocycle is instrumented with inertial sensors placed on the pedals, which is used to measure the cadence developed by the user while pedal...

This work presents a comparison between two methods for spatial feature extraction applied on a s... more This work presents a comparison between two methods for spatial feature extraction applied on a system to recognize shoulder flexion/extension motor imagery (SMI) tasks to convey on-line control commands towards a 4 degrees-of-freedom (DoF) upper-limb robotic exoskeleton. Riemannian geometry and Common Spatial Pattern (CSP) are applied on the filtered EEG for spatial feature extraction, which later are used by the Linear Discriminant Analysis (LDA) classifier for motor imagery (MI) recognition. Three bipolar EEG channels were used on six healthy subjects to acquire our database, composed of two classes: rest state and shoulder flexion/extension MI. Our system achieved a mean accuracy (ACC) of 75.12% applying Riemannian, with the highest performance for Subject S01 (ACC = 89.68%, Kappa = 79.37%, true positive rate (TPR) = 87.50%, and FPR < 8.13%). In contrast, for CSP, a mean ACC of 66.29% was achieved. These findings suggest that unsupervised methods for feature extraction, such ...