ArticleBrain sciences2019
EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy.
Article in Brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- EEG classification for neurological disorders using frequency band deciles.Scientific reports · 2025Article
- Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.Scientific reports · 2025Article
- Neurophysiological Approaches to Lie Detection: A Systematic Review.Brain sciences · 2025Review
- Multi-scale convolutional transformer network for motor imagery brain-computer interface.Scientific reports · 2025Article
- The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.Brain sciences · 2025Review
- A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.Brain sciences · 2024Article
- Internet of things based smart framework for the safe driving experience of two wheelers.Scientific reports · 2024Article
- A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.Frontiers in neuroscience · 2024Article
- The applied principles of EEG analysis methods in neuroscience and clinical neurology.Military Medical Research · 2023Review
- Deep temporal networks for EEG-based motor imagery recognition.Scientific reports · 2023Article
- OculusGraphy: Signal Analysis of the Electroretinogram in a Rabbit Model of Endophthalmitis Using Discrete and Continuous Wavelet Transforms.Bioengineering (Basel, Switzerland) · 2023Article
- Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.Sensors (Basel, Switzerland) · 2023Article
- Detection of motor imagery based on short-term entropy of time-frequency representations.Biomedical engineering online · 2023Article
- Motor imagery EEG signal classification with a multivariate time series approach.Biomedical engineering online · 2023Article
- A Literature Review Research on Monitoring Conditions of Mechanical Equipment Based on Edge Computing.Applied bionics and biomechanics · 2022Article
- Using an Optimization Algorithm to Detect Hidden Waveforms of Signals.Sensors (Basel, Switzerland) · 2021Article
- Article
- Deep Feature MiningFrontiers in bioengineering and biotechnology · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
The classification recognition rate of motor imagery is a key factor to improve the performance of brain-computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support vector machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging.
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