Posterior Probability Support Vector Machine Applied in Motor Imagery Classification

2011 ◽  
Author(s):  
Ying-ying Jiao ◽  
Xiao-pei Wu
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Posterior Probability ◽  
Support Vector ◽  
Posterior Probability Support

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Sahar Zolfaghari ◽  
Mohammad Hamiruce Marhaban ◽  
Siti Anom Ahmad ◽  
Asnor Juraiza Ishak ◽  
Pegah Khosropanah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Classification Model ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Non Linear ◽  
Left And Right

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface

2011 ◽  
Vol 3 (3) ◽  
pp. 43-56 ◽  
Author(s):  
Ling Zou ◽  
Xinguang Wang ◽  
Guodong Shi ◽  
Zhenghua Ma
Keyword(s):  
Support Vector Machine ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Average Power ◽  
Computer Interface ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Reconstructed Signal

Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the sixth level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.

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