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.

翻译：轻度创伤性脑损伤（mTBI）是一种常见且早期诊断困难的疾病。眼动功能障碍是mTBI的明确生物标志物，这推动了既能捕捉眼动行为又能反映潜在神经生理活动的便携式工具开发。本研究提出一种初步框架，通过整合脑电图（EEG）与增强现实（AR）前庭/眼动筛查（VOMS）任务，来估计受试者的特异性眼动响应时间。经过带通滤波和平均参考预处理后的EEG信号，采用冗余离散小波变换（RDWT）驱动的深度神经框架进行分析。RDWT系数经过可训练的零相位卷积滤波处理，并通过逆RDWT重构至时域，随后利用二维卷积层和基于卷积长短期记忆的解码模块进行通道间时域与空域滤波。消融研究表明，小波域滤波作为有效的去噪策略可提升预测性能。基于滑动窗口的预测结果经皮尔逊相关系数（≥0.5）验证，并采用动态时间规整（DTW）估计眼动响应时间。DTW衍生指标显示所有VOMS任务均存在显著受试者间差异（经Mann-Whitney U检验证实）。互相关分析进一步揭示任务依赖性时间行为：追踪任务呈现反应性跟踪模式，而扫视任务则表现出预期性响应特征。总体而言，追踪任务对区分时序差异最具信息价值，证实了基于RDWT的EEG特征结合DTW指标在多模态mTBI评估中的应用潜力。