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Seizure Detection Using Deep Learning, Information Theoretic Measures And Factor Graphs, Bahareh Salafian
Seizure Detection Using Deep Learning, Information Theoretic Measures And Factor Graphs, Bahareh Salafian
Electronic Thesis and Dissertation Repository
Epilepsy is a common neurological disorder that disrupts normal electrical activity in the brain causing severe impact on patients’ daily lives. Accurate seizure detection based on long-term time-series electroencephalogram (EEG) signals has gained vital importance for epileptic seizure diagnosis. However, visual analysis of these recordings is a time-consuming task for neurologists. Therefore, the purpose of this thesis is to propose an automatic hybrid model-based /data-driven algorithm that exploits inter-channel and temporal correlations. Hence, we use mutual information (MI) estimator to compute correlation between EEG channels as spatial features and employ a carefully designed 1D convolutional neural network (CNN) to extract …
Visual Cues For Semi-Autonomous Control Of Transradial Prosthetics, Mena S.A. Kamel
Visual Cues For Semi-Autonomous Control Of Transradial Prosthetics, Mena S.A. Kamel
Electronic Thesis and Dissertation Repository
Upper-limb prosthetics are typically driven exclusively by biological signals, mainly electromyography (EMG), where electrodes are placed on the residual part of an amputated limb. In this approach, amputees must control each arm joint iteratively, in a proportional manner. Research has shown that sequential control of prosthetics usually imposes a cognitive burden on amputees, leading to high abandonment rates. This thesis presents a control system for upper-limb prosthetics, leveraging a computer vision module capable of simultaneously predicting objects in a scene, their segmentation mask, and a ranked list of the optimal grasping locations. The proposed system shares control with an amputee, …
Music For Self-Attention, Jeffrey A T Lupker
Music For Self-Attention, Jeffrey A T Lupker
Electronic Thesis and Dissertation Repository
Creating an artificial intelligence aid for music composers requires a practical and modular approach that allows the composer to manipulate the technology as needed in the search for new sounds and ideas. Many existing approaches fail to capture the interest of composers as they are limited beyond their demonstrative purposes or allow minimal interaction with the composer. Score-Transformer (ST) demonstrates a practical integration of artificial intelligence to aid in the creation of new music by working seamlessly alongside any popular notation software. Furthermore, ST can be trained by the user with additional works (including their own compositions), fine-tuning it and …