ArticleFrontiers in neuroscience2017
Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The classification of walking and phases of gait using EEG: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton.Sensors (Basel, Switzerland) · 2025Article
- Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Article
- Prediction of gait intention from pre-movement EEG signals: a feasibility study.Journal of neuroengineering and rehabilitation · 2020Article
- Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study.Frontiers in neurorobotics · 2020Article
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Authors and funding
4 authors.
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Abstract
The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability. Methods are compared with the Weighted Discriminator, a recently developed comparison index. The tool developed can improve the rehabilitation process associated with lower limb exoskeletons with the help of a Brain-Machine Interface.
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