Removal of ocular artifacts from multichannel EEG signal using wavelet enhanced ICA

Paradeshi, K. P.; Kolekar, Uttam D.

doi:10.1109/icecds.2017.8390150

Cited by 9 publications

(6 citation statements)

References 1 publication

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“….. (12) where y1, ..., yn is a separated signal, H is signal entropy, I(y) represents the mutual information computed by the concept of differential entropy between n signals, and is a constant value (0.0001).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The main goal of this algorithm is to obtain pure EEG signals and extract the eye blinking artefact reference. The performance of EEG signal separation techniques is measured by the interference signal ratio (ISR) in the simulated data [12], that is, the input signal is known. However, in real EEG data, the ISR measure is not applicable because no information is available about the original sources.…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

Ahmed

AlShemmary

2020

IEEE Access

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Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (ℎlong, ℎshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (ℎlong, ℎshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

Ahmed

AlShemmary

2020

IEEE Access

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“…Therefore, for our task, the removal of EMG and EOG artifacts from raw EEG signals can be considered the top issue to address. There are already many algorithms ( Narasimhan and Dutt, 1996 ; Jung et al, 2000 ; Schlögl et al, 2007 ; Ferdousy et al, 2010 ; Vos et al, 2010 ; Safieddine et al, 2012 ; Sweeney et al, 2012 ; Teng et al, 2014 ; Zhao et al, 2014 ; Chen et al, 2017 ; Paradeshi et al, 2017 ; Yang et al, 2017 ) for removing these two artifacts (summarized in Table 1 ), the BSS-based techniques are widely proposed because they do not require a priori knowledge and reference electrodes for EMG/EOG signals acquisition and they could separate related artifacts from EEG signal by statistical inference. Among them, CCA-based methods which more effective than ICA-based methods and other filters, taking advantage of the fact that the autocorrelation coefficient of EEG is larger than that of EMG, so it is possible to separate task-related EEG and EMG artifacts.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults

Jiang

Siriaraya

Choi

et al. 2022

Front. Aging Neurosci.

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Reminiscence and conversation between older adults and younger volunteers using past photographs are very effective in improving the emotional state of older adults and alleviating depression. However, we need to evaluate the emotional state of the older adult while conversing on the past photographs. While electroencephalogram (EEG) has a significantly stronger association with emotion than other physiological signals, the challenge is to eliminate muscle artifacts in the EEG during speech as well as to reduce the number of dry electrodes to improve user comfort while maintaining high emotion recognition accuracy. Therefore, we proposed the CTA-CNN-Bi-LSTM emotion recognition framework. EEG signals of eight channels (P3, P4, F3, F4, F7, F8, T7, and T8) were first implemented in the MEMD-CCA method on three brain regions separately (Frontal, Temporal, Parietal) to remove the muscle artifacts then were fed into the Channel-Temporal attention module to get the weights of channels and temporal points most relevant to the positive, negative and neutral emotions to recode the EEG data. A Convolutional Neural Networks (CNNs) module then extracted the spatial information in the new EEG data to obtain the spatial feature maps which were then sequentially inputted into a Bi-LSTM module to learn the bi-directional temporal information for emotion recognition. Finally, we designed four group experiments to demonstrate that the proposed CTA-CNN-Bi-LSTM framework outperforms the previous works. And the highest average recognition accuracy of the positive, negative, and neutral emotions achieved 98.75%.

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“…As such, this approach is a better approach for the pre-decomposition of shipboard EMR signals. Signal processing methods based on the combination of EMD and ICA have already found widespread use in the removal of electroencephalography (EEG) artifacts [11]- [13] and fault diagnosis [14]- [16]. In Reference [17], ensemble empirical mode decomposition (EEMD) was used to decompose multivariate data, and the intrinsic mode functions (IMFs) that contain artifactual components were screened based on the entropy and kurtosis of the IMFs.…”

Section: Introductionmentioning

confidence: 99%

A Method for Blind Source Separation of Multichannel Electromagnetic Radiation in the Field

2020

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Considering the multichannel instability, spectral overlap and strong interference of electromagnetic radiation signals in the integrated electric propulsion systems of ships, a new method is proposed which combines multivariate empirical mode decomposition (MEMD) with independent component analysis (ICA) for synchronous blind source separation of multichannel electromagnetic radiation in the field. In order to construct virtual channels, noise-aided MEMD is first applied to decompose multichannel data in this approach. Then the comprehensive screening algorithm is used to filter the intrinsic mode functions (IMFs) produced by the decomposition procedure. Finally, the new multivariate input signal is analyzed after screening using ICA to obtain the original electromagnetic radiation source signal. This method is able to effectively addresses several limitations of EMD including the mode mixing problem, its inability to handle multichannel data, as well as the indeterminacy problem in ICA. The efficacy of the MEMD-ICA algorithm was evaluated using simulated signals and real world data from a cruise ship, and comparing ensemble empirical mode decomposition-ICA(EEMD-ICA). INDEX TERMS Blind source separation, electromagnetic environment, independent component analysis, integrated electric propulsion system, multivariable empirical mode decomposition.

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Removal of ocular artifacts from multichannel EEG signal using wavelet enhanced ICA

Cited by 9 publications

References 1 publication

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

Electroencephalogram Signal Eye Blink Rejection Improvement Based on the Hybrid Stone Blind Origin Separation and Particle Swarm Optimization Technique

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults

A Method for Blind Source Separation of Multichannel Electromagnetic Radiation in the Field

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