Biomedical Engineering - Theses
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ItemDecoding upper-limb kinematics from electrocorticography( 2017)Brain-computer interfaces (BCIs) are technologies for assisting individuals with motor impairments. Activity from the brain is recorded and then processed by a computer to control assistive devices. The prominent method for recording neural activity uses microelectrodes that penetrate the cortex to record from localized populations of neurons. This causes a severe inflammatory response, making this method unsuitable after approximately 1-2 years. Electrocorticography (ECoG), a method of recording potentials from the cortical surface, is a prudent alternative that shows promise as the basis of a clinically viable BCI. This thesis investigates aspects of ECoG relevant to the translation of BCI devices: signal longevity, motor information encoding, and decoding intended movement. Data was assessed from a first-in-human ECoG device trial to quantify changes in ECoG over multiple years. The mean power, calculated daily, was steady for all patients. It was demonstrated that the device could consistently record ECoG signal statistically distinct from noise up to approximately 100 Hz for the duration of the study. Therefore, long-term implanted ECoG can be expected to record movement-related high-gamma signals from humans for many years without deterioration of signal. ECoG was recorded from patients undertaking a two-dimensional center-out task. This data was used to generate encoder-decoder directional tuning models to describe and predict arm movement direction from ECoG. All four patients demonstrated channels that were significantly tuned to the direction of motion. Significant tuning was found across the cortex and was not focused on primary motor areas. Decoding significantly above chance with a population-vector approach was achieved in three of the four patients. Decoding accuracy was significantly improved by weighting the population vector by each channel's tuning signal-to-noise ratio. Hence, directional tuning exists in high-frequency ECoG during movement preparation, and movement angle can be decoded using population vector methods. Having confirmed the existence of direction-related information in the recorded data, artificial neural network models were created to decode intended movement direction. A convolutional neural network (CNN) model had significantly higher decoding accuracy than a fully connected model for all four patients for decoding movement direction. Training models on data from all patients and testing on a single patient improved decoding performance for all but the best performing patient with the CNN model. Decoding using data from multiple time-points with a CNN model and averaging the results boosted accuracy when using the mode of the outputs. Overall, it was demonstrated that artificial neural network models can decode intended movement direction from ECoG recordings of a two-dimensional center-out task. This thesis presents results that demonstrate ECoG has the desired signal properties for a clinically-relevant BCI. ECoG is shown to be robust over multiple years, encode direction-related information and can be decoded with high accuracy.