ICA With CWT and <i>k</i>-means for Eye-Blink Artifact Removal From Fewer Channel EEG

Maddirala, Ajay Kumar; Veluvolu, Kalyana C.

doi:10.1109/tnsre.2022.3176575

Cited by 22 publications

(7 citation statements)

References 50 publications

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“…To verify the effectiveness of the algorithm, the proposed CBAM-CNN method is compared with six existing methods. The comparison methods are as follows: canonical correlation analysis (CCA) 36 , 37 , FBCCA, CCA-Gaussian naive Bayes (GNB) 38 , CCA-Supporting Vector Machine (SVM) 39 , CCA-Continue Wavelet Transform (CWT)-SVM 40 , 41 , and CNN. According to the comparison of experimental results of different kernels of SVM, SVM kernel is set as linear kernel for an optimal performance.…”

Section: Resultsmentioning

confidence: 99%

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network

Lei,

Dong,

Guo

et al. 2024

Sci Rep

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For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1–2 s time window, the accuracy of CBAM-CNN is 0.0201–0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1–1 s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87 bit/min, which is 227.53 bit/min-503.41 bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.

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Section: Resultsmentioning

confidence: 99%

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network

Lei,

Dong,

Guo

et al. 2024

Sci Rep

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“…In comparative tests, the ERASE algorithm removed an average of 26% more EMG artifacts from EEG signals than the traditional ICA algorithm. In [ 66 ], a new denoising framework was devised and merges ICA with the continuous wavelet transform (CWT). CWT and the K-means algorithm are used to map the detected blink artifact.…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

“…CWT and the K-means algorithm are used to map the detected blink artifact. EEG information is then preserved while denoising through ICA [ 66 ].…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Chaddad

Kateb

et al. 2023

Sensors

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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“…Currently, the algorithm relies on frequency band selection and uses multiple features to enhance robustness [12,14,15,28,32]. In future works, there is an aspiration to integrate the algorithm with pertinent techniques [33][34][35] This collaborative approach is anticipated to provide a more comprehensive solution to mitigate the effects of intentional disturbances.…”

Section: Effect Of Selecting Band In Embedded Systemmentioning

confidence: 99%

Common Spatial Pattern and Riemannian Manifold-Based Real-Time Multiclass Motor Imagery EEG Classification

Shyu,

Huang,

Tung

et al. 2023

IEEE Access

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Several motor imagery classification methods have been developed and achieve higher accuracy. Machine learning (ML) based algorithms utilizing manually designed features often encounter robustness issues, leading to diminished accuracy. While deep learning (DL) based algorithms exhibit promising accuracy, their extensive computational requirements present challenges in implementing them on portable devices, thereby restricting their practical applications. In this paper, we improve the ML-based algorithm's feature robustness problems by combining common spatial patterns with Riemannian tangent space mapping, enhancing the algorithm's feature quality. Furthermore, we introduce a method that utilizes the distance between data points and the SVM hyperplane to compute category scores, thereby enhancing classifier performance. Our experiment uses the BCI Competition IV 2A, BCI Competition III 3A, and a selfrecorded dataset for subject-specific experiments to validate the algorithm's classification performance. Experimental results show that the proposed algorithm achieves the best classification performance, with an accuracy of 78.55%, 83.33%, and 57.44% for BCI Competition IV 2A, BCI Competition III 3A, and the selfrecorded dataset. Additionally, to assess the practicality of a real-time portable application, we implemented the proposed algorithm on Raspberry Pi and Jetson Nano, measuring their computation time and peak memory usage. The results demonstrate that our algorithm necessitates only 0.08 to 0.3 seconds of computation time and employs a mere 15MB of memory.INDEX TERMS Electroencephalograph, motor imagery, common spatial pattern, Riemannian tangent space, filter banks, support vector machine.

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ICA With CWT and k-means for Eye-Blink Artifact Removal From Fewer Channel EEG

Cited by 22 publications

References 50 publications

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Common Spatial Pattern and Riemannian Manifold-Based Real-Time Multiclass Motor Imagery EEG Classification

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