Deep Coupled Kuramoto Oscillatory Neural Network (DcKONN): A Biologically Inspired Deep Neural Model for EEG Signal Analysis

Abstract

Deep neural networks applied to signal processing tasks often need specialized architectural mechanisms to capture the temporal history of input signals. Traditional approaches include recurrent loops between layers, gated units, or tapped delay lines. However, biological brains exhibit much richer dynamics, characterized by activity across multiple frequency bands (alpha, beta, gamma, delta) and phenomena such as phase locking and synchronization. Standard Recurrent Neural Networks (RNNs) are limited in their ability to represent these complex dynamical features. In this work, we introduce a novel framework called the Deep Coupled Kuramoto Oscillatory Neural Network (DcKONN), which leverages networks of nonlinear Kuramoto oscillators trained in a deep learning paradigm. The DcKONN architecture has been applied to EEG signal classifier task. Simulation results demonstrate that the proposed oscillatory neural networks achieve superior or comparable classification accuracy compared to existing state-of-the-art models. Beyond performance improvements, these models also provide valuable neurobiological insights by naturally incorporating oscillatory dynamics into their architecture.