Clasificación de señales EEG basada en representaciones bidimensionales y redes neuronales convolucionales
It is challenging to design an electroencephalogram (EEG) signal representation that efficiently represents input information to a convolutional neural network (CNN). CNN has been successfully applied to naturally represented two-dimensional data, such as images, however EEGs are time series. In thi...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | México |
| Institución: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/2001 |
| Acceso en línea: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2001 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/Design an electroencephalogram/Señales de electroencefalograma info:eu-repo/classification/Convolutional neural network/Red neuronal convolucional info:eu-repo/classification/Mental calculation/Cálculo mental info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120304 |
| Sumario: | It is challenging to design an electroencephalogram (EEG) signal representation that efficiently represents input information to a convolutional neural network (CNN). CNN has been successfully applied to naturally represented two-dimensional data, such as images, however EEGs are time series. In this work, a new two-dimensional representation of EEG signals was developed, which makes it possible to take advantage of the success of CNNs in images. The model classifies EEG signals from motor imagery or mental calculation; the preprocessing consisted of applying the common average reference (CAR) and an 8- 30Hz band pass filter. Two representations were proposed which are spectrograms with the short-time Fourier transform (STFT) and scalograms with the continuous wavelet transform (CWT). Two classifiers were used: CNN-2D and CNN-2D + LSTM. The neural networks used, in addition to automatically extracting features, also classify the EEG signal. Unlike other models, in this work the same pre-processing, the same number of channels and the same network architecture was used for all subjects in each data set. The accuracy results were 71.34 % for BCI IV-2a (four classes), 80.71 % for BCI IV-2a (two classes), 73.82 % for BCI IV-2b, 83.57 % for BCI II-III and 82.10 % for mental calculation. In addition to obtaining competitive results with the state of the art, the training and prediction time of the proposed CNNs was very low. |
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