GPU - Accelerated Parameter Selection for Neural Connectivity Analysis Devices

Synchronous oscillations represent a core mechanism for the temporal coordination of neural activity and the functional connectivity of distributed brain regions. A snapshot of this connectivity in time can be used to infer behavioral or pathological brain states such as the onset of a seizure. Implantable neuromodulation devices have been developed to detect such states and respond using electrical stimulation to influence neural activity, thereby preventing pathological symptoms. These devices compute biomarkers which continue to grow increasingly more complex and parameterizable. Combining the outputs of a diverse range of parameter combinations can lead to more precise brain state classification, but due to the low-power constraints of implanted devices only a subset can be extracted in real-time. The existing approach to choosing this subset experimentally is no longer practical as the selection must be chosen from millions of possible combinations. Presented here is a GPU-accelerated parameter search which automates the selection process for the commonly used phase locking value (PLV) biomarker. A GPU-based parameter extraction and elastic net approach achieves a 21.3x speedup when selecting the 200 most informative parameter combinations from a search space of over 1.2 million, resulting in a 6875x reduction in on-device processing complexity.