Spectral Analysis of EEG Data for Ocular Artifact Removal Using Wavelet Transform Technique

Srinanthini, R. K.; Srinivasan, Pramila; Arun, Shivanandan

doi:10.1109/icraecc43874.2019.8995021

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…For convenience and to decreasing processing time, we first employ the common average reference (CAR) and then filter raw signals with frequencies between 7 and 35 Hz, which overlap with the main rhythmic components of ERS/ERD with regard to the MI-EEG [33]. Muscular and ocular artifacts [34] are removed through the plug-in toolbox.…”

Section: A Preprocessing Of Acquired Datamentioning

confidence: 99%

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Yang

Wang

et al. 2020

IEEE Access

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Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-tonoise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.INDEX TERMS brain-computer interface, discriminative and representative deep learning, feature fusion, convolution neural network, stacked sparse autoencoder

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Section: A Preprocessing Of Acquired Datamentioning

confidence: 99%

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Yang

Wang

et al. 2020

IEEE Access

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“…Following the wavelet transform, the EEG signal was reconstructed and denoised before the wavelet coefficients were estimated using the Bayesian approach [6]. Literature [7] combined the wavelet denoising method and minimum mean square error (MMSE), designed an adaptive threshold selector, proposed an adaptive threshold-based LMS algorithm, and carried out denoising experiments on simulated signals by boosting the format of the wavelet transform algorithm, which can effectively reduce noise, improve the signal-to-noise ratio, and have a more accurate denoising effect than traditional denoising methods. Donoho [8] and Johnstone then proposed the theory of wavelet systolic denoising [9], an algorithm that fully uses the properties of the orthogonal wavelet basis and combines the properties of the orthogonal wavelet with the different properties of the signal and noise, which can effectively eliminate interference.…”

Section: Introductionmentioning

confidence: 99%

An adaptive noise removal method for EEG signals

Chu

Wang

Zhang

et al. 2022

J. Phys.: Conf. Ser.

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EEG signals are the response to the electrophysiological activity of brain nerve cells in the cerebral cortex, but the collected EEG signal generally contains noise. In order to effectively remove the noise and retain useful information, after research and analysis, this paper proposes an improved EEG noise-removing algorithm based on the threshold method where wavelet contraction is used. The improved adaptive threshold selection algorithm makes it possible that the threshold changes with the number of layers of decomposed signal, so it can be applied flexibly in practice. The wavelet transform is used to decompose the EEG signal into multiple layers of high- and low-frequency coefficients. Adaptive threshold processing is then applied to the wavelet coefficients in accordance with the various decomposition levels, and the scaled wavelet coefficients are then reconstituted to produce the denoised EEG signal. Using the Root Mean Square Error (RMSE) and Signal Noise Ratio (SNR) as quantitative measures of the denoising effect Through trials, the improved threshold method, hard and soft threshold method, and adaptive threshold method were contrasted. The experimental findings demonstrate that the wavelet shrinkage-based improved threshold approach has a better denoising effect than the other three threshold methods.

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“…features from ECG, cough sounds, etc. Wavelet Transform had been particularly researched for its trait of decomposition, denoising and compression of signals [5][6]. In this work, continuous wavelet transform is used to decompose cough signals and the resulting coefficients in form of a scaleogram are used in a multimodal DL training pipeline.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Cough Signal Decomposition for Multimodal Classification

Agbley

Haq

et al. 2020

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

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Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency CepstralCoefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.

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Spectral Analysis of EEG Data for Ocular Artifact Removal Using Wavelet Transform Technique

Cited by 3 publications

References 2 publications

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

An adaptive noise removal method for EEG signals

Wavelet-Based Cough Signal Decomposition for Multimodal Classification

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