Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network

Tang, Xianlun; Li, Wei; Li, Xingchen; Ma, Weichang; Dang, Xiaoyuan

doi:10.1016/j.eswa.2020.113285

Cited by 125 publications

(79 citation statements)

References 23 publications

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“…1s-3s 2s-4s 3s-5s 4s-6s 5s-7s The first spatial dropping strategy is implemented by just including all electrodes belonging to the motor cortex region (that is, C3,9,10,11,C4, 14,15,16,17,18), following the spatial electrode distribution reported by [39]. We present an approach using CNN models to improve the interpretability of spatial contribution in terms of discriminating between MI tasks but preserving an adequate classification accuracy.…”

Section: A02tmentioning

confidence: 99%

“…Event-Related Synchronization to capture the channel-wise temporal dynamics of the power signal [16]; Empirical Mode Decomposition to deal with EEG nonstationarity [17,18]; and time-frequency planes using the Fourier and Wavelet Transforms are frequently extracted because they allow a more straightforward interpretation [19,20,21,22], being the latter decomposition better suited to deal with sudden changes in EEG signals. Nonetheless, the extracted 2D images tend to have substantial variability in patterns across trials due to inherent nonstationar-ity, artifacts, a poor signal-to-noise ratio of EEG signals, individual differences in cortical functioning (like subjects exhibiting activity in different frequency bands).…”

Section: Introductionmentioning

confidence: 99%

“…The agenda of the present paper is as follows: Section 2 describes the collection of MI data used for validation. [32], the spectral range is split into the following bandwidths of interest: ∆f ∈ {µ∈ [8][9][10][11][12], β low ∈ [16][17][18][19][20],β med ∈ [20][21][22][23][24],β high ∈ [24][25][26][27][28]} Hz.…”

Section: Introductionmentioning

confidence: 99%

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CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

Collazos-Huertas

Meza

Domínguez

2020

Preprint

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Interpretation of brain activity responses using Motor Imagery (MI) paradigms is vital for medical diagnosis and monitoring. Assessed by machine learning techniques, identification of imagined actions is hindered by substantial intra and inter subject variability. Here, we develop an architecture of Convolutional Neural Networks (CNN) with enhanced interpretation of the spatial brain neural patterns that mainly contribute to the classification of MI tasks. Two methods of 2D-feature extraction from EEG data are contrasted: Power Spectral Density and Continuous Wavelet Transform. For preserving the spatial interpretation of extracting EEG patterns, we project the multi-channel data using a topographic interpolation. Besides, we include a spatial dropping algorithm to remove the learned weights that reflect the localities not engaged with the elicited brain response. Obtained results in a bi-task MI database show that the thresholding strategy in combination with Continuous Wavelet Transform improves the accuracy and enhances the interpretability of CNN architecture, showing that the highest contribution clusters over the sensorimotor cortex with differentiated behavior between μ and β rhythms.

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Section: A02tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

Collazos-Huertas

Meza

Domínguez

2020

Preprint

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“…As a consequence of the nonstationarity and nonlinearity of acquired EEG data [ 11 ], the MI brain activity shows considerable variations in complexity of the physiological system with dynamics affected by motor tasks that can be perceived in the pre-stimulus activity and the elicited responses. Thus, the extracted ERS/D time-courses can be modeled as the output of a nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns

Velásquez-Martínez

Caicedo-Acosta

Castellanos-Domínguez

2020

Entropy

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Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.

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“…A method based on combination of multi-beam gamma ray attenuation and dual-modal density measurement technology used radial basis function (RBF) neural network for identifying the flow pattern and determining the void fraction in gas-liquid two-phase flows independent of the liquid phase changes of gas-liquid twophase flow (Roshani et al 2017). Neural networks have also been successfully applied to electroencephalogram (EEG) signal analysis (Shrestha et al 2019;Michielli et al 2019;Tang et al 2020), financial time series analysis (Araujo et al 2019), and stock price prediction (Qiu et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A novel method to identify the flow pattern of oil–water two-phase flow

Fan

2020

J Petrol Explor Prod Technol

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This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.

show abstract

Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network

Cited by 125 publications

References 23 publications

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

CNN-based Framework using Spatial Dropping for Enhanced Interpretation of Neural Activity in Motor Imagery Classification

Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns

A novel method to identify the flow pattern of oil–water two-phase flow

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