Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

doi:10.3389/fnins.2012.00151

Cited by 115 publications

(61 citation statements)

References 33 publications

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“…Note that it is difficult to compare our results with previous results, [20,47] as our evaluation process -with the three twoclass classification scenarios and the metrics (see subsection 2.6) -differ from their methodology, which focused on training the classifier using data from session one and then testing the resulting system on data from session two, in a four-class classification problem.…”

Section: Discussioncontrasting

confidence: 47%

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Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

Vega

Sajed

Mathewson

et al. 2016

AIR

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Recognition of motor imagery tasks (MI) from electroencephalographic (EEG) signals is crucial for developing rehabilitation and motor assisted devices based on brain-computer interfaces (BCI). Here we consider the challenge of learning a classifier, based on relevant patterns of the EEG signals; this learning step typically involves both feature selection, as well as a base learning algorithm. However, in many cases it is not clear what combination of these methods will yield the best classifier. This paper contributes a detailed assessment of feature selection techniques, viz., squared Pearson's correlation (R 2 ), principal component analysis (PCA), kernel principal component analysis (kPCA) and fast correlation-based filter (FCBF); and the learning algorithms: linear discriminant analysis (LDA), support vector machines (SVM), and Feed Forward Neural Network (NN). A systematic evaluation of the combinations of these methods was performed in three two-class classification scenarios: rest vs. movement, upper vs. lower limb movement and right vs. left hand movement. FCBF in combination with SVM achieved the best results with a classification accuracy of 81.45%, 77.23% and 68.71% in the three scenarios, respectively. Importantly, FCBF determines, based on the feature set, whether a classifier can be learned, and if so, automatically identifies the subset of relevant and non-correlated features. This suggests that FCBF is a powerful method for BCI systems based on MI. Knowledge gained here about procedural combinations has the potential to produce useful BCI tool, that can provide effective motor control for the users.

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Section: Discussioncontrasting

confidence: 47%

“…[19,20] 2.1 EEG dataset This dataset consists of EEG signals recorded from nine healthy subjects performing cue-based MI of the left hand, right hand, both feet and tongue. During the experiments, the subjects sat in front of a computer screen while auditory and visual cues instructed them on the execution of the task.…”

Section: Methodsmentioning

confidence: 99%

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

Vega

Sajed

Mathewson

et al. 2016

AIR

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“…After selection, only those electrodes whose signals exhibited significant variations between seizure and seizure-free periods were adopted as features. In previous studies (Wang et al, 2012(Wang et al, , 2013, a paired Ttest was used for screening electrodes with significant differences. However, this method is limited because it must be conducted within the assumption that all samples are normally distributed.…”

Section: Feature Selectionmentioning

confidence: 99%

The detection of epileptic seizure signals based on fuzzy entropy

Xiang

et al. 2015

Journal of Neuroscience Methods

204

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“…More details can be found in [56]. The datasets were highly contaminated with ocular artifacts which is a challenging problem in practical BCI systems [57].…”

Section: Complexitymentioning

confidence: 99%

Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG‐Based Brain‐Computer Interface: A Comprehensive Study

et al. 2018

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It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.

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Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Cited by 115 publications

References 33 publications

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

The detection of epileptic seizure signals based on fuzzy entropy

Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG‐Based Brain‐Computer Interface: A Comprehensive Study

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