A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification

Liu, Zhenfei; Wang, Lina; Xu, Shijie; Lu, Kunfeng

doi:10.1016/j.compbiomed.2022.106196

Cited by 10 publications

(2 citation statements)

References 48 publications

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“…The feature extraction techniques involve the time domain and frequency domain representations of EEG signals. Among the purely time-domain features, autoregressive (AR) models are perhaps the most important [8]. Generally, the coefficients of the temporal dependencies within the signal are used as features in this case.…”

Section: Introductionmentioning

confidence: 99%

SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-Based Brain–Computer Interface

Khanna,

Chowdhury,

Dutta

et al. 2024

IEEE Sensors J.

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Common spatial pattern (CSP) is a widely used method for feature extraction in motor imagery (MI)-based brain-computer interface (BCI) development. However, the performance of traditional CSP features often lacks robustness against inter-session and inter-subject variabilities present in MI-related electroencephalogram (EEG) signals. To address this limitation, we propose a novel approach to CSP-based feature extraction, combining spectral information obtained from Welch power-spectrum (PS) estimation with temporal variations which we named here as SCSP-3. Our SCSP-3 method employs independent learning paths for the temporal and spectral features extracted through CSP. We introduce a postprocessing step that crosses the classification probabilities from these pathways using element-wise products, deriving linearly separable features. The performance of SCSP-3 is evaluated and compared to the traditional CSP approach utilizing a support vector machine (SVM) for classification following a within-subject evaluation scheme. The results demonstrate a significant improvement in average accuracy for SCSP-3 with more generalizability, as it performs equally well with datasets from healthy subjects and stroke patients. This enhanced robustness and generalizability highlight the potential of SCSP-3 as a superior alternative to traditional CSP-based feature extraction methods for achieving consistent performance across different subject categories.

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Section: Introductionmentioning

confidence: 99%

SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-Based Brain–Computer Interface

Khanna,

Chowdhury,

Dutta

et al. 2024

IEEE Sensors J.

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show abstract

“…Power spectral density algorithm (PSD) is also being used to extract EEG signals' features. Liu et al proposed a time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm to obtain high resolution power spectral density (PSD) features [12]. Meanwhile, methods based on multiple entropy have also been applied to feature extraction in MI-EEG task.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Motor Imagery EEG via Discrete Wavelet Transform and Generalized Maximum Fuzzy Membership Difference Entropy: A Comparative Study

et al. 2023

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Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability is heavily dependent on the performance of feature extraction procedure. In this paper, a feature extraction method based on generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for the feature extraction of EEG signals. The influence of different distance calculation methods, embedding dimensions and tolerances were studied to find the best configuration of GMFMDE for the feature extraction of MI–EEG. The gradient boosting decision tree (GBDT) classifier was used to classify the features extracted from GMFMDE and DWT. The average classification accuracy of 93.71% and the maximum classification accuracy of 96.96% were obtained, which proved the effectiveness of the proposed feature extraction method for EEG signal feature extraction.

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Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

Siddiqui

Chaurasia

Shandilya

2023

J Cancer Res Clin Oncol

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A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification

Cited by 10 publications

References 48 publications

SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-Based Brain–Computer Interface

SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-Based Brain–Computer Interface

Feature Extraction of Motor Imagery EEG via Discrete Wavelet Transform and Generalized Maximum Fuzzy Membership Difference Entropy: A Comparative Study

Classification of lung cancer computed tomography images using a 3-dimensional deep convolutional neural network with multi-layer filter

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