A new phase-based feature extraction method for four-class motor imagery classification

Tosun, Mustafa; Çetin, Osman

doi:10.1007/s11760-021-02035-9

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Finally, the mean accuracy rate of 86.43% and 87.14% was achieved by evaluating ANFIS and PNN classifiers [25]. The instantaneous phase-based features in [26] were obtained by applying the Hilbert transform to four types of IMF signals of the same type in each of the 22 EEG channels. Finally, a long short-term memory (LSTM) network has been operated to classify the four-class MI features and a maximum mean accuracy rate of 89.89% has been accomplished [26].…”

Section: Introductionmentioning

confidence: 99%

“…The instantaneous phase-based features in [26] were obtained by applying the Hilbert transform to four types of IMF signals of the same type in each of the 22 EEG channels. Finally, a long short-term memory (LSTM) network has been operated to classify the four-class MI features and a maximum mean accuracy rate of 89.89% has been accomplished [26]. Ji et al in [27] first decomposed EEG signal by DWT and then an appropriate sub-band signal is applied to EMD.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Task EEG Signal Classification Using Correlation-Based IMF Selection and Multi-Class CSP

2023

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In the context of motor imagery (MI)-based brain-computer interface (BCI) systems, a great amount of research has been studied for attaining higher classification performance by extracting discriminative features from MI-based electroencephalogram (EEG) signals. In this study, we propose an innovative approach for classifying multi-class MI-EEG signals, which consists of a signal processing technique based on empirical mode decomposition (EMD) and multi-class common spatial patterns (MCCSP). Specifically, after applying the EMD, we propose selecting the best intrinsic mode functions (IMF) as the substitution to the original EEG signal for the next stage of processing. The metric we used for the selection is based on the cross-correlation of each decomposed IMF with the original signal. Next, we extend the CSP algorithm to the MCCSP to be utilized as the feature extractor. We applied our technique to the BCI competition IV (2a). Results revealed that the proposed technique improved classification accuracy significantly compared to the original case when applying MCCSP directly to the original EEG channel data. Moreover, the K-nearest neighbor (KNN) achieved the highest mean classification accuracy rate of 91.28%. Our findings suggest that a promising elevated classification accuracy of 96.71% can be achieved by raising the feature dimension through MCCSP. Compared to state-of-the-art algorithms, the performance of the proposed method is highly convincing and motivating for future studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Task EEG Signal Classification Using Correlation-Based IMF Selection and Multi-Class CSP

2023

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“…Hongtao Wang et al fuse the PLV with the one-versusthe-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of motor imagery (MI) classification. Mustafa Tosun et al [14] use sliding windows to obtain the eigenvalues of the PLV in the intrinsic mode functions (IMFs) of each channel, classifying four motor image tasks and demonstrating the high relevance of the PLV in these types of tasks.…”

Section: Introductionmentioning

confidence: 99%

A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems

Martín-Chinea

Gómez‐González

Sánchez

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification. Conclusion: This paper shows how our PLVbased filtering between EEG channels could improve the performance of a BCI.

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Motor imagery classification using sparse nonnegative matrix factorization and convolutional neural networks

Chaudhary

Varshney²,

Srivastava

et al. 2022

Neural Comput & Applic

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A new phase-based feature extraction method for four-class motor imagery classification

Cited by 5 publications

References 40 publications

Multi-Task EEG Signal Classification Using Correlation-Based IMF Selection and Multi-Class CSP

Multi-Task EEG Signal Classification Using Correlation-Based IMF Selection and Multi-Class CSP

A New PLV-Spatial Filtering to Improve the Classification Performance in BCI Systems

Motor imagery classification using sparse nonnegative matrix factorization and convolutional neural networks

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