Training Autoencoders for feature extraction of EEG signals for motor imagery
Abstract: Electroencephalography (EEG) is a common technique used to read brain activity from an individual, and can be used for a wide range of applications, one example is during the rehab process of stroke victims. Loss of motor function is a common side effect of strokes, and the EEG signals can show if sufficient activation of the part of the brain related to the motor function that the patient is training has been achieved. Reading and understanding such data manually requires extensive training. This thesis proposes to use machine learning to automate the process of determining if sufficient activation has been achieved. The process consists of a Long Short Term Memory (LSTM) Autoencoder that trains to extract features of the EEG data to be used for classification using various machine learning classification methods. In order to answer the research questions: “How to extract features from EEG signals using Autoencoders?” “Which supervised machine learning algorithm identifies as the best classification based on the features generated by the Autoencoder?” The results show that the accuracy varies greatly from individual to individual, and that the number of features created by the Autoencoder for the classification algorithms to work with has a large impact on accuracy. The choice of classification algorithm played a role for the result as well, with Support Vector Machine (SVM) performing the best, but had less impact than the previously mentioned factors.
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