Harmonic enhancement to optimize EOG based ocular activity decoding: A hybrid approach with harmonic source separation and EEMD

Abstract

Intelligent robotic systems for patients with motor impairments have gained significant interest over the past few years. Various sensor types and human-machine interface (HMI) methods have been developed; however, most research in this area has focused on eye-blink-based binary control with minimal electrode placements. This approach restricts the complexity of HMI systems and does not consider the potential of multiple-activity decoding via static ocular activities. These activities pose a decoding challenge due to non-oscillatory noise components, such as muscle tremors or fatigue. To address this issue, a hybrid preprocessing methodology is proposed that combines harmonic source separation and ensemble empirical mode decomposition in the time-frequency domain to remove percussive and non-oscillatory components of static ocular movements. High-frequency components are included in the harmonic enhancement process. Next, a machine learning model with dual input of time-frequency images and a vectorized feature set of consecutive time windows is employed, leading to a 3.8% increase in performance as compared to without harmonic enhancement in leave-one-session-out cross-validation (LOSO). Additionally, a high correlation is found between the harmonic ratios of the static activities in the Hilbert-Huang frequency spectrum and LOSO performances. This finding highlights the potential of leveraging the harmonic characteristics of the activities as a discriminating factor in machine learning-based classification of EOG-based ocular activities, thus providing a new aspect of activity enrichment with minimal performance loss for future HMI systems.

Keywords

• Electrooculogram
• Signal processing
• Harmonic source separation
• Ensemble empirical mode decomposition
• Hilbert-Huang transform
• Deep learning