Kassiri, HosseinMuneeb, Abdul2025-04-102025-04-102024-08-272025-04-10https://hdl.handle.net/10315/42747This thesis presents the design, development, and hardware implementation of neuromorphic spiking convolutional neural networks (SNNs) for patient-specific seizure detection. Leveraging the energy-efficient, event-driven nature of SNNs, the work achieves a low-power, low-latency, high-accuracy processing unit for multi-channel implantable brain-machine interfaces. Initial validation on publicly available datasets using spiking convolutional neural networks (SCNNs) demonstrates average sensitivities and specificities of 83.02% and 86.31%, respectively, with a false positive rate of 0.69/hour. The 1-bit Integer-Net SCNN achieves sensitivity comparable to 32-bit CNNs while improving memory efficiency by 27× and energy efficiency by 98.6%, consuming just 1.28 μJ/classification. Further optimization tailored to patient-specific seizure detection on STM32H747 microcontrollers and Intel Loihi simulators achieves sensitivities of 92.2% and specificities of 97.3%. By optimizing quantization resolution, encoding schemes, and implementing SCNNs in HDL, this thesis advances energy-efficient seizure detection, contributing to adaptive brain interfaces and the broader field of neuromorphic computing.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Electronic Thesis or Dissertation2025-04-10Spiking neural networksEpilepsyConvolutional neural networksHardwareIntegrated CircuitsOn-chipNeuroscienceFPGAVerilog