Dataset for Neuronal Multi Unit Activity Processing With Metal Oxide Memristive Devices

Intra-cortical brain-machine interfaces, able to decode neural activity in real-time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single-neuron spike detection require high processing rates and power, hindering they up-scaling for neurons-population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold-based neural activity detection. MIS leverages analogue multi-state switching properties of metal-oxide RRAM to compress neural inputs by encoding above-threshold events in resistance displacement, facilitating efficient data down-sampling in the post-processing, enabling low-power, high-channel systems. Initially tested on spikes and local field potentials, here MIS has been adapted to process multi-unit activity envelope (eMUA) - the envelope of entire spiking activity - which has recently been proposed as crucial input for real-time neuro-prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal-oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform-agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, we show that it can reduce total system power consumption to below 1µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional CMOS front-ends. This eMUA-MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use.