83 research outputs found
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. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). Methods: Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. Results: The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. Conclusion: These findings contribute to the development of more safe and practical BCI.Fil: Floriano, Alan. Universidade Federal do EspĂrito Santo; BrasilFil: Delisle Rodriguez, Denis. Universidade Federal do EspĂrito Santo; BrasilFil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de IngenierĂa. Departamento de ElectrĂłnica y Automática. Gabinete de TecnologĂa MĂ©dica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Juan; ArgentinaFil: Bastos Filho, Teodiano Freire. Universidade Federal do EspĂrito Santo; Brasi
Un enfoque de aprendizaje profundo para estimar la frecuencia respiratoria del fotopletismograma
This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.Este trabajo presenta una metodologĂa para entrenar y probar una red neuronal profunda (Deep Neural Network – DNN) con datos de fotopletismografĂas (Photoplethysmography – PPG), con la finalidad de llevar a cabo una tarea de regresiĂłn para estimar la frecuencia respiratoria (Respiratory Rate – RR). La arquitectura de la DNN está basada en un modelo utilizado para inferir la frecuencia cardĂaca (FC) a partir de señales PPG ruidosas. Dicho modelo se ha optimizado a travĂ©s de algoritmos genĂ©ticos. En las pruebas realizadas se usaron BIDMC y CapnoBase, dos conjuntos de datos de acceso abierto. Con CapnoBase, la DNN logrĂł un error de la mediana de 1,16 respiraciones/min, que es comparable con los mĂ©todos analĂticos reportados en la literatura, donde el mejor error es 1,1 respiraciones/min (excluyendo el 8 % de datos más ruidosos). Por otro lado, el conjunto de datos BIDMC aparenta ser más desafiante, ya que el error mĂnimo de la mediana de los mĂ©todos reportados en la literatura es de 2,3 respiraciones/min (excluyendo el 6 % de datos más ruidosos). Para este conjunto de datos la DNN logra un error de mediana de 1,52 respiraciones/min
Human-machine interfaces based on EMG and EEG applied to robotic systems
<p>Abstract</p> <p>Background</p> <p>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. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well.</p> <p>Results</p> <p>Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively.</p> <p>Conclusion</p> <p>Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.</p
Un enfoque de aprendizaje profundo para estimar la frecuencia respiratoria del fotopletismograma
Este trabajo presenta una metodologĂa para entrenar
y probar una red neuronal profunda (Deep Neural
Network – DNN) con datos de fotopletismografĂas
(Photoplethysmography – PPG), con la finalidad de
llevar a cabo una tarea de regresiĂłn para estimar la
frecuencia respiratoria (Respiratory Rate – RR). La
arquitectura de la DNN está basada en un modelo
utilizado para inferir la frecuencia cardĂaca (FC) a
partir de señales PPG ruidosas. Dicho modelo se ha
optimizado a través de algoritmos genéticos. En las
pruebas realizadas se usaron BIDMC y CapnoBase,
dos conjuntos de datos de acceso abierto. Con CapnoBase,
la DNN logrĂł un error de la mediana de 1,16
respiraciones/min, que es comparable con los métodos
analĂticos reportados en la literatura, donde el
mejor error es 1,1 respiraciones/min (excluyendo el
8 % de datos más ruidosos). Por otro lado, el conjunto
de datos BIDMC aparenta ser más desafiante,
ya que el error mĂnimo de la mediana de los mĂ©todos
reportados en la literatura es de 2,3 respiraciones/min
(excluyendo el 6 % de datos más ruidosos). Para este
conjunto de datos la DNN logra un error de mediana
de 1,52 respiraciones/min.//This article describes the methodology used to train
and test a Deep Neural Network (DNN) with Photoplethysmography
(PPG) data performing a regression
task to estimate the Respiratory Rate (RR). The
DNN architecture is based on a model used to infer
the heart rate (HR) from noisy PPG signals, which
is optimized to the RR problem using genetic optimization.
Two open-access datasets were used in
the tests, the BIDMC and the CapnoBase. With the
CapnoBase dataset, the DNN achieved a median error
of 1.16 breaths/min, which is comparable with
analytical methods in the literature, in which the best
error found is 1.1 breaths/min (excluding the 8 %
noisiest data). The BIDMC dataset seems to be more
challenging, as the minimum median error of the literature’s
methods is 2.3 breaths/min (excluding 6 %
of the noisiest data), and the DNN based approach
achieved a median error of 1.52 breaths/min with the
whole dataset
Human-Walker Interaction on Slopes Based on LRF and IMU Sensors
Abstract-Smart Walkers should be able to safely deal with inclinations in order to become a device effectively useful in the daily life of the elderly population. This paper presents a novel model of human-walker interaction on slopes. The interaction parameters are obtained from a Laser Range Finder (LRF) and an Inertial Measurement Unit (IMU). This model is integrated into the conventional closed control loop as a supervisor block. This block modifies, based on inclinations, the control set points to provide an adaptable human-walker desired position to improve comfort and safety and enhance user's confidence in the walker. The practical evaluation shows that the parameters extracted from the natural behavior of the user and the estimated set points determined with the model proposal are highly correlated, presenting a similar trend. This correlation allows performing a more natural control
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. The B.1.1.33 lineage was the first with the highest occurrence in ES, remaining predominant until September 2020. The second predominant lineage was Gamma, representing 45% of the samples. The Delta lineage appears on the State scene, proving to be the next dominant lineage. This research allowed us to understand how the lineages advanced and were distributed in the State, which is important for future work, also making it possible to guide sanitary control measures. Data analyses were made through the GISAID database for ES State showed that the pandemic in the State has been evolving dynamically with lineage replacements over the months since the first notification
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