A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components

Dharmaprani, Dhani; Nguyen, Hoang K.; Lewis, Trent; DeLosAngeles, Dylan; Willoughby, John O.; Pope, Kenneth J.

doi:10.1109/embc.2016.7590828

Cited by 12 publications

(12 citation statements)

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“…In the EEG\MEG field, and, particularly, in BCI research, ICA methods are mostly used for artifact removal during signal Cluster 1 2 3 4 5 6 7 8 9 10 11 12 Occurrence, % 71 70 61 52 52 48 39 38 35 30 26 22 Dipolarity, % 2 9 8 8 4 3 2 3 9 8 3 preprocessing or epoch classification. They are applied for eliminating ocular (Höller et al, 2013;Dharmaprani et al, 2016;Sarin et al, 2020), motion (Zhou et al, 2016;Kobler et al, 2019), and muscle (Höller et al, 2013;Dharmaprani et al, 2016) artifacts. The most popular method used for artifact suppression is RunICA (extended infomax), likely due to its incorporation into EEGLAB (Delorme and Makeig, 2004) and BrainStorm (Tadel et al, 2011) software.…”

Section: Discussionmentioning

confidence: 99%

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

et al. 2020

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In this study, the sources of EEG activity in motor imagery brain–computer interface (BCI) control experiments were investigated. Sixteen linear decomposition methods for EEG source separation were compared according to different criteria. The criteria were mutual information reduction between the source activities and physiological plausibility. The latter was tested by estimating the dipolarity of the source topographic maps, i.e., the accuracy of approximating the map by potential distribution from a single current dipole, as well as by the specificity of the source activity for different motor imagery tasks. The decomposition methods were also compared according to the number of shared components found. The results indicate that most of the dipolar components are found by the Independent Component Analysis Methods AMICA and PWCICA, which also provided the highest information reduction. These two methods also found the most task-specific EEG patterns of the blind source separation algorithms used. They are outperformed only by non-blind Common Spatial Pattern methods in terms of pattern specificity. The components found by all of the methods were clustered using the Attractor Neural Network with Increasing Activity. The results of the cluster analysis revealed the most frequent patterns of electrical activity occurring in the experiments. The patterns reflect blinking, eye movements, sensorimotor rhythm suppression during the motor imagery, and activations in the precuneus, supplementary motor area, and premotor areas of both hemispheres. Overall, multi-method decomposition with subsequent clustering and task-specificity estimation is a viable and informative procedure for processing the recordings of electrophysiological experiments.

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Section: Discussionmentioning

confidence: 99%

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

et al. 2020

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“…where : s = Independent component of vector x W = Matrix weight x = Vector data To obtain n independent components, n data of x are needed [27,39]. The vector s will be the features vector of the vector x and is used for the classification process.…”

Section: =mentioning

confidence: 99%

“…The most widely used and the fastest ICA algorithm is FastICA [25,27]. FastICA is a fixed point iteration scheme to find a maximum nongaussianity value of w T x.…”

Section: =mentioning

confidence: 99%

“…Shen et al [26] have used ICA in the blind decoding of massive multiple-inputs multiple-outputs. Dharmaprani et al [27] have compared some ICA algorithms in the implementation of electroencephalogram (EEG) distinguisher from interference signals. The algorithms were joint approximate diagonalization of Eigenmatrices (JADE), Information Maximization (Infomax), and FastICA.…”

Section: Introduction mentioning

confidence: 99%

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Voice Classification Based on Fast Independent Component Analysis to Support Nuclear Power Plant Security

Budi

Suparman

2020

Atom Indo.

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National vital objects have important role in national development, so they require special protection. Nuclear power plant (NPP) is one of them. Access restriction is required to prevent the NPP from potential hazards. The restriction can be improved by using face, fingerprints, retina, iris, and voice password. The improvement will enhance the security of the NPP. This research has implemented pattern recognition and classification of voice passwords. The passwords were a, i, u, e, and o. The features vector was searched by using Fast ICA method while the pattern classification was performed by minimum Euclidean method. The purpose of this research is to recognize and classify those letter password, so people who have access to the nuclear area can be distinguished. The methodology of this research consists of input data, pre-processing data, feature extraction, and classification. Pre-processing was done by normalization, denoising, centering, and whitening. Feature extraction was performed by Fast ICA method, and classification was done by minimum Euclidean distance. The results show that Fast ICA and minimum Euclidean methods can 100 % distinguish between the employees who have access permit and those who have no access permit. When an employee with access permit says "aiueo", it will be recognized as password, whereas when an employee with no access permit says the password, it will be recognized not as the password.

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“…When obtaining independent components, other than FastICA algorithm, SOBI (Second-Order Blind Identification) which uses second-order statistics such as delayed correlation matrix, FOBI (Fourth-Order Blind Identification) which uses fourthordered statistics, Infomax which is based on maximization of entropy and JADE (Joint Approximation Diagonalization of Eigenmatrices) which is based on common diagonalization algorithms are also widely used [38]. Studies comparing the mentioned ICA algorithms in terms of efficiency and speed are available in the literature [43][44][45][46].…”

Section: Fastica Algorithmmentioning

confidence: 99%

Evaluation of Independent Components Analysis from Statistical Perspective and Its Comparison with Principal Components Analysis

Bursa

Tatlidil

2020

Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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One of the most important problems in statistics and related fields is that finding an appropriate representation of multivariate data. Here is meant by representation; to transform the data into a more visible (accessible) form. Independent Components Analysis (ICA) is a statistical method used to find the underlying components of multivariate data and makes its main structure more visible. In this respect, ICA can also be seen as an extension of the Principal Components Analysis (PCA). However, ICA, contrary to PCA, is based on statistical independence rather than unrelatedness and statistical independence is a much stronger feature than unrelatedness. In addition, while the normal distribution of the components obtained in PCA is desired, the independent components of ICA are requested not to distribute normally. In the study, although it is a multivariate statistical method, the subject of ICA, which is not well known in the field of statistics and which is mostly used in engineering, was discussed in detail and contributed to the limited statistical literature on the subject. In the application part, ICA was compared with a similar method, PCA. Both analyzes were applied to an artificial dataset and it was concluded that ICA was much more successful than PCA in detecting non-normal components.

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A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components

Cited by 12 publications

References 12 publications

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

Voice Classification Based on Fast Independent Component Analysis to Support Nuclear Power Plant Security

Evaluation of Independent Components Analysis from Statistical Perspective and Its Comparison with Principal Components Analysis

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