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arxiv: 2605.14883 · v1 · pith:UK4ZLAF3new · submitted 2026-05-14 · 📡 eess.SP · cs.HC· cs.LG

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

Shantanu Sarkar , Sai Shashank Gandavarapu , Jeff Feng , Saurabh Prasad , Reza Khanbabaie , Jose L. Contreras-Vidal This is my paper

Pith reviewed 2026-06-30 20:14 UTC · model grok-4.3

classification 📡 eess.SP cs.HCcs.LG
keywords mTBIEEGRDWTDTWVOMSocular response timedeep learningbrain computer interface
0
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The pith

An RDWT-driven neural network combined with dynamic time warping estimates ocular response times from EEG during VOMS tasks and detects inter-subject differences.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a method to estimate subject-specific ocular response times by processing EEG with an RDWT-driven deep neural network during AR-based VOMS tasks and aligning predictions with dynamic time warping. Validation uses Pearson correlation thresholds, after which DTW metrics demonstrate significant inter-subject differences supported by statistical tests. Pursuit tasks stand out as informative for timing distinctions, with cross-correlation showing reactive versus anticipatory patterns across tasks. This multimodal approach combines brain signals and eye movement behavior to aid early mTBI detection in a portable format. The ablation confirms the value of wavelet filtering for better predictions.

Core claim

The framework uses RDWT coefficients fed into trainable zero-phase convolutional filters, followed by inverse transform, 2D convolutions, and conv-LSTM decoding to generate predictions from pre-processed EEG. These sliding-window predictions are validated with Pearson correlation of at least 0.5 and then aligned via DTW to estimate response times. Mann-Whitney U tests on the DTW metrics confirm significant inter-subject differences in all VOM tasks, while cross-correlation analysis indicates reactive tracking in pursuit tasks and anticipatory responses in saccades, highlighting the potential of RDWT-based EEG features combined with DTW metrics for multimodal mTBI assessment.

What carries the argument

The RDWT-driven deep neural framework that extracts and denoises EEG features for temporal alignment with dynamic time warping to compute ocular response times.

If this is right

  • DTW-derived metrics can distinguish timing differences between subjects in VOMS tasks.
  • Pursuit tasks provide particularly useful information for identifying response time variations.
  • The combination of RDWT-based EEG features and DTW supports multimodal assessment of mTBI.
  • Task-specific temporal patterns emerge, with pursuit showing reactive and saccades anticipatory behavior.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could enable portable, non-invasive screening tools for mTBI in settings without access to eye-tracking equipment.
  • Extending the framework to include direct eye-tracking validation might strengthen the accuracy claims for clinical use.
  • Similar approaches could be tested for other conditions involving oculomotor or vestibular dysfunction.
  • The sliding-window approach might be adapted for real-time applications in neurorehabilitation.

Load-bearing premise

That neural network predictions of EEG features, validated only by correlation thresholds without simultaneous eye-tracking recordings, accurately reflect true ocular response times.

What would settle it

A study measuring the same subjects' eye movements with both the EEG framework and a gold-standard eye tracker during identical VOMS tasks, then comparing the derived response times.

Figures

Figures reproduced from arXiv: 2605.14883 by Jeff Feng, Jose L. Contreras-Vidal, Reza Khanbabaie, Sai Shashank Gandavarapu, Saurabh Prasad, Shantanu Sarkar.

Figure 1
Figure 1. Figure 1: Department of Defense (DoD) worldwide traumatic brain injury [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: EEG acquisition setup and electrode configuration. (a) Participant [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Normalized target trajectory computed as the L2 distance between [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Signal quality of raw and preprocessed EEG data was verified using trial-wise mean and standard deviation plots across channels and participants. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Model architecture illustrating RDWT-inspired band-specific filtering [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Block diagram of the analysis pipeline, including preprocessing, fea [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: 4) Ocular Response Time Analysis: The Dynamic Time Warping (DTW) distance is commonly used to analyze temporal delays and alignment differences between two time series, especially when they have similar shapes but are misaligned in time [23]. To quantify ocular response time within the valid windows, the DTW distance was computed between the extracted EEG features from the trained M0 model (after depth-wis… view at source ↗
Figure 7
Figure 7. Figure 7: Validation-set correlation-based outcomes for models M0–M2. Stacked bars show the percentage of acceptable (r [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 10
Figure 10. Figure 10: Violin plots showing DTW distance distributions between extracted [PITH_FULL_IMAGE:figures/full_fig_p006_10.png] view at source ↗
Figure 9
Figure 9. Figure 9: PSD plots of extracted EEG features for valid windows, showing [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Normalized cross-correlation dynamics (unitless) between DTW [PITH_FULL_IMAGE:figures/full_fig_p007_11.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a BCI framework that processes EEG during AR-based VOMS tasks via an RDWT-driven DNN (trainable zero-phase convolutional filtering, inverse RDWT, 2D conv + conv-LSTM decoder) to generate sliding-window predictions of ocular response. These predictions are thresholded at Pearson r >= 0.5 and aligned with DTW to extract subject-specific response times; Mann-Whitney U tests on the resulting DTW metrics are reported to show significant inter-subject differences across VOMS tasks, with pursuit tasks highlighted as most informative for mTBI assessment.

Significance. If the DTW-derived latencies can be shown to track true oculomotor timing, the work would offer a portable multimodal signal-processing approach to mTBI screening that fuses neurophysiological features with behavioral timing. The ablation on wavelet-domain filtering and the cross-correlation analysis of task-dependent (reactive vs. anticipatory) behavior are positive elements. However, the absence of any direct validation against simultaneous eye-tracking or clinical timing standards leaves the central quantitative claim unsupported.

major comments (2)
  1. [Abstract] Abstract (final paragraph) and Results: the only validation reported for the sliding-window DNN outputs is a Pearson correlation threshold of >=0.5; no mean r values, no error distributions, no ablation of the threshold itself, and no comparison to a null or baseline predictor are supplied. Because the DTW step operates directly on these outputs, this threshold alone cannot establish that the extracted response times are accurate rather than artifacts of the model.
  2. [Abstract] Abstract and Discussion: no simultaneous eye-tracking ground truth, no comparison to clinical VOMS stopwatch timings, and no reported latency error bounds are provided for the DTW-derived ocular response times. Without such anchoring, the reported Mann-Whitney U significance on inter-subject differences cannot be interpreted as reflecting true oculomotor behavior rather than systematic prediction offsets.
minor comments (2)
  1. [Abstract] The abbreviation 'VOM tasks' appears once; it should be standardized to 'VOMS tasks' throughout for consistency with the method name.
  2. The ablation study is mentioned but not located in a numbered section or table; a dedicated subsection or table summarizing the performance lift from RDWT filtering would improve traceability.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment below, agreeing where additional details or clarifications are warranted while noting the preliminary scope of this EEG-focused framework.

read point-by-point responses

  1. Referee: [Abstract] Abstract (final paragraph) and Results: the only validation reported for the sliding-window DNN outputs is a Pearson correlation threshold of >=0.5; no mean r values, no error distributions, no ablation of the threshold itself, and no comparison to a null or baseline predictor are supplied. Because the DTW step operates directly on these outputs, this threshold alone cannot establish that the extracted response times are accurate rather than artifacts of the model.

    Authors: We agree that the reported validation is limited and that additional metrics are required to substantiate the DNN outputs before DTW application. In the revised manuscript we will add mean Pearson r values with distributions across windows and subjects, an ablation study varying the threshold, and a comparison against a null or baseline predictor to show that the predictions are not artifacts. revision: yes

  2. Referee: [Abstract] Abstract and Discussion: no simultaneous eye-tracking ground truth, no comparison to clinical VOMS stopwatch timings, and no reported latency error bounds are provided for the DTW-derived ocular response times. Without such anchoring, the reported Mann-Whitney U significance on inter-subject differences cannot be interpreted as reflecting true oculomotor behavior rather than systematic prediction offsets.

    Authors: We acknowledge that the study lacks simultaneous eye-tracking or clinical stopwatch ground truth, as the current work is confined to EEG signal processing during AR-VOMS tasks. The Mann-Whitney results therefore reflect differences in the model's predicted timings rather than validated oculomotor latencies. We will revise the Discussion to explicitly state this limitation and frame the findings as demonstrating the potential of the RDWT-DTW pipeline rather than clinical equivalence. Latency error bounds cannot be computed without ground truth. revision: partial

standing simulated objections not resolved
  • Absence of simultaneous eye-tracking ground truth and clinical VOMS timing comparisons, which would require new multimodal data collection not present in the current EEG-only study.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The provided abstract and context describe a pipeline of RDWT preprocessing, trainable convolutional filtering, 2D conv + conv-LSTM decoding for sliding-window predictions (validated externally via Pearson r >= 0.5 threshold), followed by separate DTW alignment on those outputs to derive response-time metrics, with Mann-Whitney tests applied afterward. No equations, self-definitional loops, fitted-input-as-prediction reductions, or load-bearing self-citations are present in the text. The DTW step operates on independently generated model outputs and task data rather than re-deriving its own inputs by construction. The derivation remains self-contained against external benchmarks and statistical validation steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract-only review limits visibility into free parameters or invented entities; the framework appears to rest on standard EEG filtering and wavelet assumptions plus the unstated premise that neural predictions correlate with actual ocular motor timing.

axioms (2)
  • domain assumption Band-pass filtering and average referencing produce usable EEG for ocular response estimation
    Stated in abstract as pre-processing step without further justification
  • domain assumption Pearson correlation >= 0.5 on sliding windows validates the neural predictions as proxies for ocular timing
    Used to accept predictions before DTW; location: abstract validation paragraph

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discussion (0)

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Reference graph

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