BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework

2026-05-14

AI Summary

This study addresses the challenge of early diagnosis in mild traumatic brain injury (mTBI) by proposing an objective assessment method based on oculomotor dysfunction. The approach integrates electroencephalography (EEG) with augmented reality–based eye-tracking tasks and introduces a novel multimodal deep neural network that combines trainable zero-phase convolutional filtering in the redundant discrete wavelet transform (RDWT) domain with dynamic time warping (DTW). This framework enables robust extraction of spatiotemporal features and accurate estimation of individualized oculomotor response times. The method significantly improves prediction accuracy, achieving Pearson correlation coefficients of at least 0.5 in sliding-window response time forecasting. Moreover, DTW-derived metrics exhibit pronounced inter-individual variability across all eye-tracking tasks, with the pursuit task demonstrating the highest discriminative power.

Abstract

Mild traumatic brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in its early stages. Oculomotor dysfunction is a well-established marker of mTBI, motivating the development of portable tools that capture both eye-movement behavior and underlying neurophysiology. In this work, we present an initial framework that integrates electroencephalogram (EEG) with augmented-reality (AR)-based Vestibular/Ocular Motor Screening (VOMS) tasks to estimate subject-specific ocular response times. Pre-processed EEG signals, obtained through band-pass filtering and average referencing, are analyzed using a Redundant Discrete Wavelet Transform (RDWT)-driven deep neural framework. The RDWT coefficients are subjected to trainable zero-phase convolutional filtering and reconstructed into the time domain via inverse RDWT, followed by channel-wise temporal and spatial filtering using 2D convolution layers and convolutional-LSTM-based decoding. An ablation study demonstrates that wavelet-domain filtering serves as an effective denoising strategy, improving prediction performance. Sliding-window predictions were validated using Pearson correlation (>= 0.5), and Dynamic Time Warping (DTW) was subsequently used to estimate ocular response times. DTW-derived metrics revealed significant inter-subject differences across all VOM tasks, supported by Mann-Whitney U tests. Cross-correlation analysis further revealed task-dependent temporal behaviors: pursuit tasks exhibited reactive tracking, whereas saccades showed anticipatory responses. Overall, the results highlight pursuit tasks as particularly informative for distinguishing timing differences and demonstrate the potential of RDWT-based EEG features combined with DTW metrics for multimodal mTBI assessment.

Problem

• mild traumatic brain injury
• ocular response time
• oculomotor dysfunction
• EEG
• Vestibular/Ocular Motor Screening

Innovation

• Redundant Discrete Wavelet Transform
• Dynamic Time Warping
• Deep Neural Framework
• Ocular Response Time
• Multimodal BCI