Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

doi:10.1155/2007/82069

Cited by 85 publications

(74 citation statements)

References 30 publications

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“…Among the existing BSS algorithms, four main ones have emerged to specifically tackle the difficult problem of EEG data denoising. Independent component analysis (ICA) was successfully applied to EEG denoising for muscular activity [4][5][6][7][8][9][10][11][12][13]. More recently, another BSS approach called Canonical Correlation Analysis (CCA) was also proposed to remove muscle artifacts from EEG and improve the interpretation of ictal epochs [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

150

122

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Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.

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Section: Introductionmentioning

confidence: 99%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

150

122

View full text Add to dashboard Cite

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“…Many authors have pointed that EOG artifacts have characteristic topography and PSD features, which are the main features used by the experts to remove artifacts when they visually inspect EEG [13]; Halder et al [7]. To identify the OAC automatically, in the present study, the PSD and topography of all the ICs were extracted as the recognition features.…”

Section: Feature Extractionmentioning

confidence: 95%

Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Gao

Lin

Yang

et al. 2010

Neural Comput & Applic

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Frequent occurrence of ocular artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In the present paper, a novel and robust technique is proposed to eliminate ocular artifacts from EEG signals in real time. Independent Component Analysis (ICA) is used to decompose EEG signals. The features of topography and power spectral density of those components are extracted. Moreover, we introduce manifold learning algorithm, a recently popular dimensionality reduction technique, to reduce the dimensionality of initial features, and then those new features are fed to a classifier to identify ocular artifacts components. A k-nearest neighbor classifier is adopted to classify components because classification results show that manifold learning with the nearest neighbor algorithm works best. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove ocular artifacts effectively from EEG signals with little distortion of the underlying brain signals and be satisfied the real-time application.

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“…Eighteen EEG electrodes (Table 1) were located according to the international 10-20 system and were referenced to the electrode Cz. Because EOG and EMG artifacts were strong on the Fp1, Fp2, O1, and O2 electrodes, they were not selected for analysis [19,20]. The data acquisition equipment is given in Figure 1.…”

Section: Data Set Descriptionmentioning

confidence: 99%

Common spatial pattern-based feature extraction from the best time segment of BCI data

Aydemir

2016

Turk J Elec Eng & Comp Sci

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Abstract:Feature extraction is one of the most crucial stages in the field of brain computer interface (BCI). Because of its ability to directly influence the performance of BCI systems, recent studies have generally investigated how to modify existing methods or develop novel techniques. One of the most successful and well-known methods in BCI applications is the common spatial pattern (CSP). In existing CSP-based methods, the spatial filters were extracted either by using the whole data trial or by dividing the trials into a number of overlapping/nonoverlapping time segments. In this paper, we developed a CSP-based moving window technique to obtain the most distinguishable CSP features and increase the classifier performance by finding the best time segment of electroencephalogram trials. The extracted features were tested by using support vector machines (SVMs). The performance of the classifier was measured in terms of classification accuracy and kappa coefficient ( κ) . The proposed method was successfully applied to the two-dimensional cursor movement imagery data sets, which were acquired from three healthy human subjects in two sessions on different days. The experiments proved that instead of using the whole data length of EEG trials, extracting CSP features from the best time segment provides higher classification accuracy and κ rates.

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Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

Cited by 85 publications

References 30 publications

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Common spatial pattern-based feature extraction from the best time segment of BCI data

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