Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

Davies, Simon; James, Christopher J.

doi:10.1109/embc.2013.6610822

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The experimental data came from BCI Competition 2008 datasets 2b data [13], including EEG data of nine subjects, all of whom were right-handed with normal or corrected normal vision. The experimental process is shown in Figure 2.…”

Section: Methodsmentioning

confidence: 99%

EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy

Dong

et al. 2019

Brain Sciences

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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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Section: Methodsmentioning

confidence: 99%

EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy

Dong

et al. 2019

Brain Sciences

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“…Univariate EMD, however suffers from the problem of mode-mixing wherein similar frequencies occur in different IMFs [15]. To overcome this issue, a multichannel version namely, multivariate EMD (MEMD) has been investigated to show its comparative advantage [16], [15], [17], [18]. The MEMD allows to achieve high localization of information pertaining to specific frequency-bands.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Subject Specific Intrinsic Mode Function Selection for Enhancing Two-Class EEG-Based Motor Imagery-Brain Computer Interface

et al. 2019

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The electroencephalogram (EEG) signals tend to have poor time-frequency localization when analysis techniques involve a fixed set of basis functions such as in short-time Fourier transform (STFT) and wavelet transform (WT). These signals also exhibit highly non-stationary characteristics and suffer from low signal-to-noise ratio (SNR). As a result, there is often poor task detection accuracy and high error rates in designed braincomputer interfacing (BCI) systems. In this paper, a novel preprocessing method is proposed to automatically reconstruct the EEG signal by selecting the intrinsic mode functions (IMFs) based on a median frequency measure. Multivariate empirical mode decomposition (MEMD) is used to decompose the EEG signals into a set of IMFs. The reconstructed EEG signal has high SNR and contains only information correlated to a specific motor imagery task. The common spatial pattern (CSP) is used to extract features from the reconstructed EEG signals. The linear discriminant analysis (LDA) and support vector machine (SVM) have been utilized in order to classify the features into left hand motor imagery (LHMI) and right hand motor imagery (RHMI) tasks. Our experimental results on the BCI competition IV dataset 2A show that the proposed method with fifteen channels outperforms bandpass filtering with twenty-two channels (> 1%) and by > 9 % (p = 0.0078) with raw EEG signals, > 13% (p = 0.0039) with empirical mode decomposition (EMD) based filtering and > 17 % (p = 0.0039) with discrete wavelet transform (DWT) based filtering.

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Using Empirical Mode Decomposition with Spatio-Temporal dynamics to classify single-trial Motor Imagery in BCI

Davies

James

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper introduces a new signal processing method called Spatio-Temporal Multivariate Empirical Mode Decomposition (ST-MEMD). It is a new variation of Empirical Mode Decomposition (EMD) that takes spatial and temporal information into account simultaneously rather than processing each signal source in isolation. The original and new methods were tested on single-trial electroencephalogram data with a two-class problem, in this case data using the Motor Imagery paradigm in brain-computer interfacing. However, whilst ST-MEMD retained the increase in sensitivity and specificity from adding spatial data, the new temporal data made no meaningful difference in terms of performance.

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Novel use of Empirical Mode Decomposition in single-trial classification of motor imagery for use in brain-computer interfaces

Cited by 6 publications

References 9 publications

EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy

EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy

An Automatic Subject Specific Intrinsic Mode Function Selection for Enhancing Two-Class EEG-Based Motor Imagery-Brain Computer Interface

Using Empirical Mode Decomposition with Spatio-Temporal dynamics to classify single-trial Motor Imagery in BCI

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