Single-channel EOG-based human-machine interface with exploratory assessments using harmonic source separation

TL;DR

This study explores the use of a single-channel EOG device for complex activity detection in human-machine interfaces, utilizing harmonic source separation to enhance classification accuracy and real-time performance.

Contribution

It introduces a novel approach using harmonic source separation in EOG signals to improve activity detection and classification in HMI systems with minimal sensing.

Findings

High-frequency harmonic components improve activity contrast.

The 2D CNN achieved 72.35% accuracy in activity prediction.

Real-time system tested successfully with user feedback.

Abstract

There have been many studies on intelligent robotic systems for patients with motor impairments, where different sensor types and different human-machine interface (HMI) methods have been developed. However, these studies fail to achieve complex activity detection at the minimum sensing level. In this paper, exploratory approaches are adopted to investigate ocular activity dynamics and complex activity estimation using a single-channel EOG device. First, the stationarity of ocular activities during a static motion is investigated and some activities are found to be non-stationary. Further, no statistical difference is found between the envelope sequences in the temporal domain. However, when utilized as an alternative to a low-pass filter, high-frequency harmonic components in the frequency domain are found to improve contrasting ocular activities and the performance of the EOG-HMI-based activity detection system substantially. The activities are trained with different classifiers and their prediction success is evaluated with leave-one-session-out cross-validation. Accordingly, the two-dimensional CNN model achieved the highest performance with the accuracy of 72.35\%. Furthermore, the clustering performance is assessed using unsupervised learning and the results are evaluated in terms of how well the feature sets are grouped. The system is further tested in real-time with the graphical user interface and the scores and survey data of the subjects are used to verify the effectiveness.