Brain controlled robot navigation based on low cost EEG
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This thesis focuses on employing low cost EEG signals to control robots for navigation tasks. A data driven signal processing and machine learning framework is proposed and applied. Power Spectral Density (PSD) and Spectral Analysis are used for feature extraction, and I examined the result of Principle Component Analysis (PCA), and chose non-linear classifiers for machine learning. The algorithm for classification is Quadratic Discriminant Analysis (QDA), and achieved around 88% to 91% accuracy for five-fold cross validation. When testing with a new dataset, the accuracy is around 82%, but will be low in contaminated datasets and at varying electrode locations. I also experimented the real-time system, and most instructions are correctly classified. This thesis provides a novel system for EEG data processing, especially for situations of low cost, low channel amount equipment.