Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Classification of EEG signals: an assistance approach for remote rehabilitation for the upper limb(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Lazcano Herrera, Alicia Guadalupe; Alfaro Ponce, Mariel; emipsanchez; Chairez Oria, Jorge Isaac; González Mendoza, Miguel; Guzmán Zavaleta, Zobeida Jezabel; School of Engineering and Sciences; Campus Estado de México; Fuentes Aguilar, Rita QuetziquelRehabilitation technologies help disabled people face the many challenges in their daily lives. As a consequence, there has been an increase in the interest in developing technologies such as Human-Computer Interfaces (HCI) and Brain-Computer Interfaces (BCI). These technologies can be triggered by many biosignals and their related studies or extraction techniques, being one of these biosignals the ones related to information on brain activity. Electroencephalography represents electrical brain activity as a form of brain signal; the records produced by this technique are called electroencephalograms (EEG).This technique involves the pickup of the biopotential, the signal conditioning, the signal recording, and the signal analysis, being one of their main goals the observation and analysis of brain responses to sensor stimuli.Despite the many advantages of the use of EEG signals and other technologies for BCI composition, one of the challenges we face is the complexity of interpreting and classifying EEG signals. This is where the use of Artificial Intelligence (AI) and Machine Learning(ML) algorithms becomes crucial. The development of ML algorithms for EEG signal analysis is not just a trend but a necessity in our quest to understand and harness the power of brain signals.Nowadays, to analyze brain signals, algorithms such as Neural networks have been used, and among all the architectures available, Recurrent Neural Networks become popular because they can provide context in their predictions. In this category can be found the Long- Short Term Memory (LSTM) networks, which are NN’s with a memory block that can ”store”information. Using this ML algorithm for the analysis of EEG signals could help develop new technologies that could assist impaired people aided with technologies like remote assistance or remote rehabilitation. The present dissertation aims to apply different techniques which involve Machine Learning (ML) techniques, to analyze, process, and classify EEG signals to integrate the information derived into an application that can be used to apply remote rehabilitation aid. This dissertation is divided into two major axes: one focuses on the EEG signals and analysis and the second axis is focused on the application of ML algorithms for classifying Motor/Imagery(MI) information that could be integrated into a remote rehabilitation application. It will discuss the results obtained in the use of Time-Domain and Frequency-Domain techniques for extraction features of EEG signals in publicly available datasets (Physionet Motor/Imagery dataset) and an acquired dataset that could replicate the information found in the literature, the application of ML algorithms for feature selection, the advantages of the normalization process, the application of Neural Networks (two types, recurrent neural networks, and convolutional neural networks) to classify EEG MI information and how can this be integrated into a platform for remote rehabilitation that helps to avoid the abandonment of therapy and that offers supports to take rehabilitation measures in remote places. These results remark the use of the BiLSTM NNs for EEG MI information classification with an accuracy of 91.25% and the use of the Convolutional Neural Network SquezeenNet with a maximum accuracy reported of 92.23%