Prediction of motion trajectories based on motor imagery by a brain computer interface
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...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | Perú |
| Institución: | Pontificia Universidad Católica del Perú |
| Repositorio: | PUCP-Institucional |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.pucp.edu.pe:20.500.14657/146072 |
| Acceso en línea: | http://hdl.handle.net/20.500.12404/11605 |
| Access Level: | acceso abierto |
| Palabra clave: | Interfaces cerebro-computadora Interfaces de computadora--Control https://purl.org/pe-repo/ocde/ford#2.00.00 |
| Sumario: | 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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