Hybrid CMOS/Memristor Front-End for Multiunit Activity Processing

Epileptic seizure prediction could help patients stay safe and provide them with opportunities to prevent seizures in advance. This can be realised by a complete system that captures the intracortical neuronal signals from the implantable device, processes the recorded data for discriminating seizures and transfers the information to the personal advisory device. Seizures can be discriminated by monitoring the counts of population spikes and we proposed a spike detection front-end for this application. The proposed discrete-time system amplifies, detects and digitises the spiking with ultra-low power and high precision with the aid of memristor as a trimming device. In this paper, we utilised the measurement methodology for the discrete-time system that combines periodic steady-state analysis and transient simulation to examine its behaviour under sources of uncertainty: noise, process corner and mismatch. The noise performance can be improved by oversampling while maintaining low power consumption. And the memristive devices are capable of compensating the inherent offset and do not induce material impact. Combining work and verification above, the system can be scaled up and/or practical implementation in the next step.