EEG efficient classification of imagined hand movement using RBF kernel SVM

Bousseta, Rihab; Tayeb, Salma; Ouakouak, Issam El; Gharbi, Mourad; Regragui, Fakhita; Himmi, Mohammed Majid

doi:10.1109/sita.2016.7772278

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…Spectro-temporal representations are fed into an SVM with an RBF kernel to recognize different MI tasks. The SVM constructs an optimal hyperplane by using the most significant margin to deal with the two-class problem (Bousseta et al, 2016;Liu et al, 2012). The classification function is expressed as:…”

Section: Classification 241 Svm Classifiermentioning

confidence: 99%

“…The parameter c denotes the ability of the classifier to punish misclassification, and the parameter g denotes a gamma distribution of the transformed data (Dong et al, 2017). The parameters that directly affect the generalization ability of the classifier balance the relationship between minimizing the training error and maximizing the margin between classes (Bousseta et al, 2016). The most suitable hyperplane is found by adjusting the parameters c and g, which plays an essential role in the classification effect of the classifier (Liu et al, 2012;Özbeyaz et al, 2011).…”

Section: Classification 241 Svm Classifiermentioning

confidence: 99%

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Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Zheng

Shan

et al. 2020

J. Integr. Neurosci.

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One of the challenges in brain-computer interface systems is obtaining motor imagery recognition from brain activities. Brain-signal decoding robustness and system performance improvement during the motor imagery process are two of the essential issues in brain-computer interface research. In conventional approaches, ineffective decoding of features and high complexity of algorithms often lead to unsatisfactory performance. A novel method for the recognition of motor imagery tasks is developed based on employing a modified S-transforms for spectro-temporal representation to characterize the behavior of electrocorticogram activities. A classifier is trained by using a support vector machine, and an optimized wrapper approach is applied to guide selection to implement the representation selection obtained. A channel selection algorithm optimizes the wrapper approach by adding a cross-validation step, which effectively improves the classification performance. The modified S-transform can accurately capture event-related desynchronization/event-related synchronization phenomena and can effectively locate sensorimotor rhythm information. The optimized wrapper approach used in this scheme can effectively reduce the feature dimension and improve algorithm efficiency. The method is evaluated on a public electrocorticogram dataset with a recognition accuracy of 98% and an information transfer rate of 0.8586 bit/trial. To verify the effect of the channel selection, both electrocorticogram and electroencephalogram data are experimentally analyzed. Furthermore, the computational efficiency of this scheme demonstrates its potential for online brain-computer interface systems in future cognitive tasks.

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Section: Classification 241 Svm Classifiermentioning

confidence: 99%

Section: Classification 241 Svm Classifiermentioning

confidence: 99%

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Zheng

Shan

et al. 2020

J. Integr. Neurosci.

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“…Ghumman et al investigated the classification performance of SVM with a polynomial kernel in multiclass motor imagery EEG [40]. Bousseta et al used SVM with RBF kernel to classify the EEGs of imagined hand movements [41]. These studies reported a classification range of 67-92.8% [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning

Zhang

Huang

et al. 2022

Applied Sciences

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Electroencephalography (EEG)-based measurements of fine tactile sensation produce large amounts of data, with high costs for manual evaluation. In this study, an EEG-based machine-learning (ML) model with support vector machine (SVM) was established to automatically evaluate post-stroke impairments in fine tactile sensation. Stroke survivors (n = 12, stroke group) and unimpaired participants (n = 15, control group) received stimulations with cotton, nylon, and wool fabrics to the different upper limbs of a stroke participant and the dominant side of the control. The average and maximal values of relative spectral power (RSP) of EEG in the stimulations were used as the inputs to the SVM-ML model, which was first optimized for classification accuracies for different limb sides through hyperparameter selection (γ, C) in radial basis function (RBF) kernel and cross-validation during cotton stimulation. Model generalization was investigated by comparing accuracies during stimulations with different fabrics to different limbs. The highest accuracies were achieved with (γ = 21, C = 23) for the RBF kernel (76.8%) and six-fold cross-validation (75.4%), respectively, in the gamma band for cotton stimulation; these were selected as optimal parameters for the SVM-ML model. In model generalization, significant differences in the post-stroke fabric stimulation accuracies were shifted to higher (beta/gamma) bands. The EEG-based SVM-ML model generated results similar to manual evaluation of cortical responses to fabric stimulations; this may aid automatic assessments of post-stroke fine tactile sensations.

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“…The model is simpler and more computationally efficient than other methods [4]. Bousseta et al used continuous wavelet transform and principal component analysis to process EEG data and input the processed features into a support vector machine with Radial Belief Function (RBF) kernel and achieved an average accuracy of 92.75% on the EEG dataset [5]. However, the performance of support vector machines was influenced by the kernel functions, and different kernel functions give different results.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines

Wang¹,

Liu²,

Zhang³

et al. 2022

Journal on Artificial Intelligence

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Deep kernel mapping support vector machines have achieved good results in numerous tasks by mapping features from a low-dimensional space to a high-dimensional space and then using support vector machines for classification. However, the depth kernel mapping support vector machine does not take into account the connection of different dimensional spaces and increases the model parameters. To further improve the recognition capability of deep kernel mapping support vector machines while reducing the number of model parameters, this paper proposes a framework of Lightweight Deep Convolutional Cross-Connected Kernel Mapping Support Vector Machines (LC-CKMSVM). The framework consists of a feature extraction module and a classification module. The feature extraction module first maps the data from low-dimensional to high-dimensional space by fusing the representations of different dimensional spaces through cross-connections; then, it uses depthwise separable convolution to replace part of the original convolution to reduce the number of parameters in the module; The classification module uses a soft margin support vector machine for classification. The results on 6 different visual datasets show that LC-CKMSVM obtains better classification accuracies on most cases than the other five models.

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EEG efficient classification of imagined hand movement using RBF kernel SVM

Cited by 18 publications

References 20 publications

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Decoding spectro-temporal representation for motor imagery recognition using ECoG-based brain-computer interfaces

Evaluation of Post-Stroke Impairment in Fine Tactile Sensation by Electroencephalography (EEG)-Based Machine Learning

A Framework of Lightweight Deep Cross-Connected Convolution Kernel Mapping Support Vector Machines

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