Feature Extraction Algorithm based on CSP and Wavelet Packet for Motor Imagery EEG signals

Feng, Guocan; Lü, Hao; Nuo, Gao

doi:10.1109/siprocess.2019.8868635

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…The extraction of features is an important step in the classification of EEG signals (Amin et al, 2017 ). Some works have used the combination of WPD and CSP to extract features and have achieved better results compared to the use of CSP only (Yang et al, 2012 ; Feng et al, 2019 ). In this research, we use a combination of WPD and CSP to extract important resources for the LSTM-based neural decoder.…”

Section: Methodsmentioning

confidence: 99%

“…The first neural decoder is based on LSTM, where the characteristics of frequency, time, and space of the signals are extracted separately, through the combination of wavelet packet decomposition (WPD) and common spatial pattern (CSP). This step of pre-processing was chosen based on results presented in the literature (Yang et al, 2012 ; Feng et al, 2019 ). The second decoder was called EEGNet-LSTM and combines the features of both models, extracting the characteristics simultaneously with the classification.That decoder is similar to the best decoder implemented by Wang L. et al ( 2020 ), however with differences in the architecture and selection of hyperparameters of the decoder and strategies for data pre-processing.…”

Section: Introductionmentioning

confidence: 99%

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Empirical comparison of deep learning methods for EEG decoding

Oliveira¹,

Rodrigues²

2023

Front. Neurosci.

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Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches. Therefore, it is believed that these techniques can also improve the process of decoding brain signals in BMI systems. In this work, we present the implementation of two deep learning-based decoders and we compared the results with other state of art deep learning methods. The first decoder uses long short-term memory (LSTM) recurrent neural network and the second, entitled EEGNet-LSTM, combines a well-known neural decoder based on convolutional neural networks, called EEGNet, with some LSTM layers. The decoders have been tested using data set 2a from BCI Competition IV, and the results showed that the EEGNet-LSTM decoder has been approximately 23% better than the competition-winning decoder. A Wilcoxon t-test showed a significant difference between the two decoders (Z = 2.524, p = 0.012). The LSTM-based decoder has been approximately 9% higher than the best decoder from the same competition. However, there was no significant difference (Z = 1.540, p = 0.123). In order to verify the replication of the EEGNet-LSTM decoder on another data, we performed a test with PhysioNet's Physiobank EEG Motor Movement/Imagery dataset. The EEGNet-LSTM presented a higher performance (0.85 accuracy) than the EEGNet (0.82 accuracy). The results of this work can be important for the development of new research, as well as EEG-based BMI systems, which can benefit from the high precision of neural decoders.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical comparison of deep learning methods for EEG decoding

Oliveira¹,

Rodrigues²

2023

Front. Neurosci.

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“…Robinson et al [ 14 ] used the wavelet-CSP algorithm to classify fast and slow hand movements. Feng et al [ 15 ] proposed a feature extraction algorithm based on CSP and wavelet packet for motor imagery EEG signals, and Yang et al [ 16 ] proposed subject-based feature extraction using the fisher WPD-CSP method. The second type of method, the spatial spectrum filter is optimized simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Aiming to resolve the problem of large calculation and time-consumption of Wavelet-CSP [ 13 , 14 ], WPD-CSP [ 15 , 16 ], and FBCSP [ 11 ] methods, we have proposed three new feature extraction methods, namely CSP-Wavelet, CSP-WPD, and CSP-FB method. Firstly, the original EEG signals are pre-processed, including time window selection and band-pass filtering.…”

Section: Introductionmentioning

confidence: 99%

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Zhang

Zhu

Zhang

et al. 2020

Sensors

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The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

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Multi-domain fusion deep graph convolution neural network for EEG emotion recognition

Wang

Yan

et al. 2022

Neural Comput & Applic

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Feature Extraction Algorithm based on CSP and Wavelet Packet for Motor Imagery EEG signals

Cited by 10 publications

References 6 publications

Empirical comparison of deep learning methods for EEG decoding

Empirical comparison of deep learning methods for EEG decoding

The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Multi-domain fusion deep graph convolution neural network for EEG emotion recognition

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