DSpace Repository :: Browsing by Subject "INTERFAZ CEREBRO COMPUTADORA"

Browsing by Subject "INTERFAZ CEREBRO COMPUTADORA"

Now showing 1 - 8 of 8

Detección de pestañeos basado en análisis de una señal de movimiento ocular
(2020-09-22) Cifuentes, Ignacio; Lynch, Ezequiel; Buscaglia, Matías; Ramele, Rodrigo
"En el siguiente trabajo presentamos un estudio preliminar de protocolos de comunicación accesible, basado en movimientos oculares o faciales. Para ello, investigamos la utilización de Interfaces Cerebro Computadora Híbridas, en adelante, Brain/Neural Computer Interfaces (BNCI), para aplicar soluciones tecnológicas que ayuden en la rehabilitación de pacientes que necesitan sistemas Alternativos de Comunicación Aumentada, en adelante, Augmentative and Alternative Communications (AAC). Buscamos entender las problemáticas dentro de los centros de rehabilitación y cómo este trabajo puede generar un impacto. Utilizando BNCI, estudiamos la detección de pestañeos basado en el análisis de una señal de electrooculografía y desarrollamos un prototipo para este motivo. El mismo, es un clasificador de señales producidas por movimiento ocular utilizando técnicas de procesamiento y, en pos de poder evaluar la factibilidad de generar productos finales a partir de este, realizamos un análisis de resultados del mismo para medir su efectividad. De ser factible, este clasificador será la base para crear un sistema de AAC alternativo a los tradicionales que trabaje con los pestañeos, ayudando a la rehabilitación de pacientes que lo necesiten. Finalmente, en relación a los costos de tecnología en rehabilitación, analizamos y gestionamos varias alternativas de búsqueda de fondos para el trabajo y futuros proyectos asociados."
EEG waveform analysis of P300 ERP with applications to brain computer interfaces
(2018-11) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
"The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition."
EEG waveform identification based on deep learning techniques
(2022) Ail, Brian Ezequiel; Ramele, Rodrigo
"The use of Brain-Computer Interfaces can provide substantial improvements to the quality of life of patients with diseases such as severe Amyotrophic lateral sclerosis that cause Locked-in syndrome, by creating new avenues in which these people can communicate and interact with the outside world. The P300 speller is an interface which provide the patients the ability to spell letters and eventually words, so that they can speak while unable to use their mouth. The P300 speller works by reading signals from the brain using an Electroencephalogram. Traditionally, these signals were plotted and interpreted by specialized technicians or neurologists, but the development of Machine learning algorithms for classification allow the computers to perform this analysis and detect the P300 signals, which is an Event Related Potential triggered when certain stimuli such as a bright light is triggered on a place that the patient is focused on. In this thesis we used a Convolutional Neural Network to train multi-channel EEG readings, and attempted to detect P300 signals from a P300 speller. The results are corroborated against a public ALS dataset."
Histogram of gradient orientations of EEG signal plots for brain computer interfaces
(2018) Ramele, Rodrigo; Santos, Juan Miguel; Villar, Ana Julia
"Brain Computer Interface (BCI) or Brain Machine Interfaces (BMI), has proved the feasibility of a distinct non-biological communication channel to transmit information from the Central Nervous System (CNS) to a computer device. Promising success has been achieved with invasive BCI, though biocompatibilities issues and the complexity and risks of surgical procedures are the main drive to enhance current non-invasive technologies. Electroencephalography (EEG) is the most widespread method to gather information from the CNS in a non-invasive way. Clinical EEG has traditionally focused on temporal waveforms, but signal analysis methods which follow this path have been neglected in BCI research. This Thesis proposes a method and framework to analyze the waveform, the shape of the EEG signal, using the histogram of gradient orientations, a fruitful technique from Computer Vision which is used to characterize image local features. Inspiration comes from what traditionally electroencephalographers have been doing for almost a century: visually inspecting raw EEG signal plots."
Histogram of gradient orientations of signal plots applied to P300 detection
(2019-07) Ramele, Rodrigo; Villar, Ana Julia; Santos, Juan Miguel
"The analysis of Electroencephalographic (EEG) signals is of ulterior importance to aid in the diagnosis of mental disease and to increase our understanding of the brain. Traditionally, clinical EEG has been analyzed in terms of temporal waveforms, looking at rhythms in spontaneous activity, subjectively identifying troughs and peaks in Event-Related Potentials (ERP), or by studying graphoelements in pathological sleep stages. Additionally, the discipline of Brain Computer Interfaces (BCI) requires new methods to decode patterns from non-invasive EEG signals. This field is developing alternative communication pathways to transmit volitional information from the Central Nervous System. The technology could potentially enhance the quality of life of patients affected by neurodegenerative disorders and other mental illness. This work mimics what electroencephalographers have been doing clinically, visually inspecting, and categorizing phenomena within the EEG by the extraction of features from images of signal plots. These features are constructed based on the calculation of histograms of oriented gradients from pixels around the signal plot. It aims to provide a new objective framework to analyze, characterize and classify EEG signal waveforms. The feasibility of the method is outlined by detecting the P300, an ERP elicited by the oddball paradigm of rare events, and implementing an offline P300-based BCI Speller. The validity of the proposal is shown by offline processing a public dataset of Amyotrophic Lateral Sclerosis (ALS) patients and an own dataset of healthy subjects."
RSVP speller implementation for OpenViBE as a brain-computer interface
(2020-12-16) Grethe Borsani, Juan; Grabina, Martín Ezequiel; Ramele, Rodrigo
"In neuroscience, the P300 wave is a positive deflection in the human event-related potential. It is considered to be an endogenous potential, as its occurrence links not to the physical attributes of a stimulus, but to a person’s reaction to it. Therefore, it is possible to train a signal processing and classifier pipeline to learn how a person's brain reacts to different stimulus and allow a person to trigger actions in a computer using only brainwaves. In this context, OpenViBE is an open source software platform that enables to design, test and use Brain-Computer Interfaces (BCIs) such as Spellers: BCIs that allows people to write on a software by only using their brain activity. OpenViBE already includes a speller, but it cannot be used by patients with visual disabilities. Rapid serial visual presentation (RSVP) is a new paradigm of spelling, gaze-independent. Therefore, the objective of this work is to analyze, expose and explain the implementation of this new methodology as a new plugin in the OpenViBE platform, for all the BCI community worldwide."
Training a gaming agent on brainwaves online: using brain signals as feedback for reinforcement learning
(2020-12-12) Abelenda, Marcos; Vázquez, Agustín Ignacio; Manganaro Bello, Santiago; Ramele, Rodrigo
"This thesis replicates and proposes an alternative method to train reinforcement learning algorithms with ErrP signals, captured through EEG, and validate the effectiveness of its use in a prototype application."
Training an agent on brainwaves: using brain signals as feedback for reinforcement learning
(2019) Moreno, Juan; Bartolomé, Francisco; Navas, Natalia; Vitali, José; Ramele, Rodrigo
"This thesis replicates and proposes an alternative method to train reinforcement learning algorithms with ErrP signals, captured through EEG, and validate the effectiveness of its use in a prototype application."

Browsing by Subject "INTERFAZ CEREBRO COMPUTADORA"

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