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In this research we propose to use EEG signal to classify two emotions (i.e., positive and negative) elicited by pictures. With power spectrum features, the accuracy rate of SVM classifier is about 85.41%. Considering each pair of channels and different frequency bands, it shows that frontal pairs of channels give a better result than the other area and high frequency bands give a better result than...
This paper describes the method for classifying multiclass motor imagery EEG signals of brain-computer interfaces (BCIs) according to the phenomena of event-related desynchronization and synchronization (ERD/ERS). The method of one-versus-one common spatial pattern (CSP) for multiclass feature extraction was employed. And we extended two different kinds of classifiers: 1) support vector machines (SVM)...
Brain-computer interface (BCI) can provide communication channels which do not depend on peripheral nerves and muscles for patients with neuromuscular disorders. The goal of the paper is to validate signal processing and classification methods for Brain-Computer Interfaces (BCIs). This paper presented a method combining wavelet with Common Spatial Pattern (CSP). Use multi-resolution analysis (MRA)...
In this paper, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence...
This work presents a multi-channel patient-independent neonatal seizure detection system based on the SVM classifier. Several post-processing steps are proposed to increase temporal precision and robustness of the system and their influence on performance is shown. The SVM-based system is evaluated on a large clinical dataset using several epoch-based and event based metrics and curves of performance...
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