Document Type

Article

Publication Date

2026

DOI

10.3390/s26051719

Publication Title

Sensors

Volume

26

Issue

5

Pages

1719

Abstract

Noise degrades both EEG and gait signals, and classical IIR filters (Butterworth, Chebyshev, elliptic) involve trade-offs between passband flatness, ripple, and roll-off. This study compared a novel exponential "Reza" filter with these designs for neural and locomotor data. We analyzed an open-source mobile brain-body imaging dataset with EEG and gait data from 49 healthy adults (EEG: 256-channel, 512 Hz; IMUs: six APDM Opals, 128 Hz). EEG channels were grand-averaged and band-pass filtered at 0.5-50 Hz, while IMU axes were averaged and band-pass filtered at 0.5-5 Hz. The outcomes were signal-to-noise ratio SNR (dB) and band-integrated Welch PSD (EEG:0.5-50 Hz; IMU:0.5-5 Hz). Repeated-measures ANOVAs tested the effect of filter types (Butterworth, Chebyshev I, elliptic, Reza) with Bonferroni-adjusted post hoc tests for the six pairwise filter comparisons (αadj = 0.0083). We reported partial eta-squared (ηp²) as the ANOVA effect size. For EEG, PSD did not differ among filters (p = 0.146), whereas SNR differed strongly (p < 0.001): Chebyshev and elliptic yielded the highest mean SNR and did not differ from each other, while both exceeded Butterworth, Reza was the lowest. For IMU, both SNR (p< 0.001) and PSD (p< 0.001) differed: Reza produced the highest mean SNR (significantly exceeding elliptic and Chebyshev), while Butterworth exceeded Chebyshev; meanwhile, IMU PSD showed a clear ordering with Reza retaining the most motion-band power, followed by Butterworth, then Chebyshev, with elliptic retaining the least. These results showed that filter choice materially shapes EEG and gait outcomes. For EEG, Chebyshev maximized SNR, while elliptic and Reza maintained comparable fidelity. For IMU gait signals, Reza matched Butterworth for denoising and preserved more signal power. Therefore, filter choice should be guided by the target outcome (SNR vs. band power) rather than a single default design.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

The human subjects data used in this analysis are available via the public dataset published by Hanada, Kalabic, and Ferris (2024) [41]. The full Reza-Filter repository (including high-pass, low-pass, and band-pass implementations, example scripts, and analysis utilities) is available at: https://github.com/Rezapousti/Reza-Filter.git, accessed on 9 March 2026.

ORCID

0009-0004-1090-9688 (Pousti), 0000-0001-9130-1172 (Russell), 0000-0001-7256-4508 (Rhea)

Original Publication Citation

Pousti, R., Russell, D. M., Monroe, D. C., & Rhea, C. K. (2026). EEG and IMU gait signal processing: A comparative assessment of the "Reza" exponential filter and classical filters. Sensors, 26(5), Article 1719. https://doi.org/10.3390/s26051719

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