A hybrid SVM and kernel function-based sparse representation classification for automated epilepsy detection in EEG signals.
Quanhong WangWeizhuang KongJitao ZhongZhengyang ShanJuan WangXiaowei LiHong PengBin HuPublished in: Neurocomputing (2023)
Keyphrases
- eeg signals
- kernel function
- sparse representation
- support vector
- signal processing
- support vector machine
- svm classification
- kernel methods
- dictionary learning
- feature space
- svm classifier
- brain computer interface
- input space
- support vectors
- sparse coding
- image classification
- polynomial kernels
- hyperplane
- eeg data
- high dimensional feature space
- kernel matrix
- image representation
- support vector machine svm
- kernel parameters
- random projections
- pattern recognition
- positive definite
- epileptic seizures
- support vector regression
- feature selection
- kernel machines
- kernel learning
- classification accuracy
- decision boundary
- multiple kernel learning
- high dimensional data
- machine learning
- gaussian kernels
- feature vectors
- reproducing kernel hilbert space
- dimensionality reduction
- image processing
- data mining
- neural network
- extracted features
- face recognition
- reduced set
- data sets