Neuromorphic Signal Classification using Organic Electrochemical Transistor Array and Spiking Neural Simulations

Neuromorphic computing is an exciting and rapidly growing field that aims to create computing systems that can replicate the complex and dynamic behavior of the human brain. Organic electrochemical transistors (OECTs) have emerged as a promising tool for developing such systems due to their unique bioelectronic properties. In this paper, we present a novel approach for signal classification using an OECT array, which exhibits multifunctional bioelectronic functionality similar to neurons and synapses linked through a global medium. Our approach takes advantage of the intrinsic device variabilities of OECTs to create a reservoir network with variable neuron-time constants and synaptic strengths. We demonstrate the effectiveness of our approach by classifying surface-electromyogram (sEMG) signals into three hand gesture categories. The OECT array performs efficient signal acquisition by feeding signals through multiple gates and measuring the response to a group of OECTs with a global liquid medium. We compare the performance of our approach with and without projecting the input on OECTs and observe a significant increase in classification accuracy from 40% to 68%. We also examined how the classification performance is affected by different selection strategies and numbers of OECTs used. Finally, we developed a spiking neural network-based simulation that mimics the OECTs array and found that OECT-based classification is comparable to the spiking neural network-based approach. Our work paves the way for the next generation of low-power, real-time, and intelligent biomedical sensing systems.