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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&#x2013;brain. Organic electrochemical transistors (OECTs) have emerged as a promising tool for developing such systems due to their unique bioelectronic properties. In this article, 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 (s-EMG) 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&#x0025; to 68&#x0025;. 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.

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