Abstract: Real-time on-device cognitive load assessment using EEG is very useful for applications like brain-computer interfaces, robotics, adaptive learning etc. Existing deep learning based models can achieve high accuracy, but due to large memory and energy requirement, those models can not be implemented on battery driven low-compute, low-memory edge devices such as wearable EEG devices. In this paper, we have used brain-inspired spiking neural networks and neuromorphic computing paradigms, that promises at least $10^4$ times less energy requirement compared to existing solutions. We have designed two different spiking network architectures and tested on two publicly available cognitive load datasets (EEGMAT \& STEW). We achieved comparable accuracy with existing arts, without performing any artifact removal from EEG signal. Our model offers $\sim8\times$ less memory requirement, $\sim10^3\times$ less computational cost and consumes maximum 0.33 $\mu$J energy per inference.