Quantum Neuromorphic Classification of EEG Brain Signals

Quantum computing has recently inspired many applications in machine learning. In this paper, we present a novel idea of combining neuromorphic computing and quantum computing to develop an advanced application for studying and analysing brain signals. We integrate a quantum spiking leaky integrate-and-fire (QLIF) neuron into a 3D spiking neural network framework designed for analysing and classifying biological data. This modified model is applied to EEG brain signals obtained from experimental measurements of a subject's wrist movements, providing a real-world demonstration of the model's capabilities. The proof-of-concept results show that the QLIF model outperforms its classical counterpart across a series of binary classification tasks, suggesting the promising compatibility of quantum neuromorphic algorithms for the spatio-temporal EEG dataset. Our illustration offers a new direction for enhancing the advantages of quantum computing in complex machine learning applications.