Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach

Miao, Minmin; Zeng, Hong; Wang, Aimin; Zhao, Changsen; Liu, Feixiang

doi:10.1016/j.jneumeth.2016.12.010

Cited by 73 publications

(39 citation statements)

References 42 publications

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“…These channels are around the sensorimotor cortex, providing a better signal-to-noise ratio than the other channels, and are thus more important in decoding MI tasks [10,35]. The results are compared with those obtained by using other feature extraction methods based on all 60 channels [34,[36][37][38][39][40][41], as well as using BP features extracted from the broad band (8-30 Hz) and full length (from the start to the end of motor imagery) EEG signals at the same three Laplacian channels. Furthermore, we also test our method with different amounts of artifacts contaminated trials to evaluate its robustness to artifacts.…”

Section: Subjectmentioning

confidence: 99%

“…Furthermore, we compared our method with the methods that were proposed in the last five years from 2012 to 2016, including sparse time-frequency segment CSP (STFSCSP) [39], KullbackLeibler (KL) divergence based CSP (KL-CSP) [38], KL divergence based local temporal common spatial patterns (KL-LTCSP) [38], discrete cosine transform (DCT) [40] and linear prediction singular value decomposition (LP-SVD) [40,41,51]. Since all these recent works used 10-fold cross-validation to test their methods, we provide our results in 10-fold cross-validation as well to compare with them in Table 3.…”

Section: Improving Classification Performance Based On Few Eeg Channelsmentioning

confidence: 99%

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Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels

Yang

Chevallier

Wiart

et al. 2017

Biomedical Signal Processing and Control

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a b s t r a c tThe essential task of a motor imagery brain-computer interface (BCI) is to extract the motor imageryrelated features from electroencephalogram (EEG) signals for classifying motor intentions. However, the optimal frequency band and time segment for extracting such features differ from subject to subject. In this work, we aim to improve the multi-class classification and to reduce the required EEG channel in motor imagery-based BCI by subject-specific time-frequency selection. Our method is based on a criterion namely Fisher discriminant analysis-type F-score to simultaneously select the optimal frequency band and time segment for multi-class classification. The proposed method uses only few Laplacian EEG channels (C3, Cz and C4) located around the sensorimotor area for classification. Applied to a standard multi-class BCI dataset (BCI competition III dataset IIIa), our method leads to better classification performance and smaller standard deviation across subjects compared to the state-of-art methods. Moreover, adding artifacts contaminated trials to the training dataset does not necessarily deteriorate our classification results, indicating that our method is tolerant to artifacts.

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Section: Subjectmentioning

confidence: 99%

Section: Improving Classification Performance Based On Few Eeg Channelsmentioning

confidence: 99%

Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels

Yang

Chevallier

Wiart

et al. 2017

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

show abstract

“…In BCI competition III dataset IV-a, all subjects (sub-dataset) has 280 trials. Although every subject consisted different train and test sets, they were combined into one dataset due to the low number of trials as used by some prior research [6], [18], [19]. This evaluation calculated after voting scheme is conducted.…”

Section: Discussionmentioning

confidence: 99%

“…Another promising study introduced by [18]. They improve CSP with sparse time-frequency segment common spatial pattern (STFSCSP) combined with Discernibility of Feature Sets (DFS) criteria that dedicated for spatial filter optimization and Weighted Naïve Bayesian Classifier (WNBC).…”

Section: Related Workmentioning

confidence: 99%

“…In spatial domain, Common Spatial Pattern (CSP) is one of most popular technique in EEG based MI classification. Therefore, many studies have been used and improve CSP such as: CSP with sparse time-frequency segment common spatial pattern [18], CSP combined with Differential Evolution as feature selection [19], CSP combined with symmetrical feature [20] and Filter Bank Regularized Common Spatial Pattern [21]. Another approach based on time-frequency signal processing technique by employing three popular wavelet technique has been conducted by [6].…”

Section: Introductionmentioning

confidence: 99%

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Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Wijaya¹,

Adji²,

Setiawan³

2018

EECSI

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Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems.

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Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

Shrestha,

Rahman,

Saha

et al. 2024

Int J Imaging Syst Tech

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Individuals who are suffering from the most severe of motor disabilities can improve their quality of life by controlling and directing mechanical and electronic devices. As for Spinal Cord Injured (SCI) patients', attempted hand movements can be classified using electroencephalography (EEG). The research aims to develop a hybrid CNN‐LSTM (Convolutional Neural Network—Long Short Term Memory) architecture for multichannel EEG signal classification. It is a challenging task to classify real‐world multichannel EEG data from SCI patients. The proposed research preprocessed the EEG data to improve the signal‐to‐noise ratio and arranged for them to extract additional information from the data. The preprocessing step includes filtering, downsampling, and artifact removal, while the postprocessing step includes time‐frequency representation and spatial information encoding. A hybrid CNN‐LSTM is used for feature extraction and classification. The proposed method has been implemented on a dataset consisting of 5 different classes of attempted hand movements from 10 SCI patients. The average classification accuracy of 92.36% is achieved for 5‐class classification. To check the global validity of the proposed network, the BCI competition IV data is classified by the proposed method and has found 92.70% overall accuracy.

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Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: An sparse regression and Weighted Naïve Bayesian Classifier-based approach

Cited by 73 publications

References 42 publications

Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels

Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Attempted movement classification of spinal cord injured patients using hybrid CNN‐LSTM networks

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