Ingeniería Mecatrónica (Mag.)

Permanent URI for this collectionhttp://98.81.228.127/handle/20.500.12404/1750

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Subject: search.filters.subject.Interfaces cerebro-computadora

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    Prediction of motion trajectories based on motor imagery by a brain computer interface
    (Pontificia Universidad Católica del Perú, 2018-03-20) Petersamer, Matthias; Haueisen, J.
    The aim of this Master's Thesis was to develop a naturally controllable BCI that can predict motion trajectories from the imagination of motor execution. The approach to reach this aim was to nd a correlation between movement and brain data, which can subsequently be used for the prediction of movement trajectories only by brain signals. To nd this correlation, an experiment was carried out, in which a participant had to do triggered movements with its right arm to four di erent targets. During the execution of the movements, the kinematic and EEG data of the participant were recorded. After a preprocessing stage, the velocity of the kinematic data in x and y directions, and the band power of the EEG data in di erent frequency ranges were calculated and used as features for the calculation of the correlation by a multiple linear regression. When applying the resulting regression parameter to predict trajectories from EEG signals, the best accuracies were shown in the mu and low beta frequency range, as expected. However, the accuracies were not as high as necessary for control of an application.
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    Development of a BCI based on real-time neural source localization
    (Pontificia Universidad Católica del Perú, 2017-10-14) Klüber, Viktor; Esch, Lorenz
    Brain-Computer-Interfaces (BCIs) provide a novel way of communication by interpreting different types of brain states. This principle of reading minds makes BCIs a challenging but at the same time fascinating topic among the different disciplines of electrophysiology and biomedical-signal-processing. This work describes the development of a non-invasive BCI approach using steadystate- visual-evoked-potentials (SSVEP) as a mental strategy. SSVEP based BCIs require an external visual stimulation, which in this work is transmitted by a LCD-screen. Consequently, a visual reactive BCI is integrated as a plug-in into the open source project MNE-CPP, which provides an extensive library for brain monitoring and processing. MNE-Scan, as a standalone software from MNE-CPP, contains the necessary real-time source localization and is used as a framework for the BCI. Moreover, an expansion with a screen keyboard device shows the BCI’s practicability. The work’s result delivers a functioning SSVEP BCI approach with an average detection accuracy of 86 %. However, it is shown, that a transition from a BCI on sensor level to a BCI on source level is challenging and requires a certain pre-development, whereby a first approach of the BCI only was realized on sensor level in this work.
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    Procesamiento de señales electroencefalográficas en un sistema embebido para una interfaz cerebro máquina
    (Pontificia Universidad Católica del Perú, 2017-05-12) Acuña Condori, Kevin José; Achanccaray Díaz, David Ronald
    Una de las tecnologías actuales que está causando gran impacto en la vida de las personas con discapacidad motora severa es el Interfaz Cerebro-Máquina(BMI, por sus siglas en inglés), sistema que permite convertir pensamiento o intención de movimiento de una persona en medios de comunicación y comandos de control de dispositivos, logrando independencia para el usuario. Sin embargo, los equipos actuales dependen de una PC que realice el procesamiento de las señales cerebrales, lo que dificulta que el sistema sea portable y de bajo costo. La presente tesis estudia y propone el uso de un sistema embebido (microcomputadora) como alternativa al uso de la PC en el BMI. Las microcomputadoras a diferencia de las PC comunes, son diseñadas para ciertos propósitos específícos, esto presenta una reducción de costo y mayor portabilidad del equipo. Con ello se pretende contribuir al desarrollo de esta nueva tecnología en el Perú haciéndolo accesible para personas de escasos recursos, lo que impactaría en la mejora de calidad de vida de las personas con discapacidad motora severa. Los resultados muestran que el sistema embebido Odroid-xu4(que cuesta 20 veces menos y es 45 veces mas liviano) puede realizar el entrenamiento de los algoritmos y el procesamiento en tiempo real de señales EEG con la misma tasa de acierto que la laptop, tardando aproximadamente 9 veces más; sin embargo estos tiempos son mínimos para aplicaciones del interfaz cerebro-máquina por lo que se demuestra que el Odroid-xu4 puede ser usado como equipo de procesamiento para una BMI portable, confiable y de bajo costo.