Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI
نویسندگان
چکیده
منابع مشابه
Semi-Supervised Clustering Approach for P300 based BCI Speller Systems
The paper presents a k-means based semi-supervised clustering approach for recognizing and classifying P300 signals for BCI Speller System. P300 signals are proved to be the most suitable Event Related Potential (ERP) signal, used to develop the BCI systems. Due to non-stationary nature of ERP signals, the wavelet transform is the best analysis tool for extracting informative features from P300...
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The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct b...
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In this paper, we address the important problem of channel selection for a P300-based brain computer interface (BCI) speller system in the situation of insufficient training data with labels. An iterative semi-supervised support vector machine (SVM) is proposed for time segment selection as well as classification, in which both labeled training data and unlabeled test data are utilized. The per...
متن کاملاستخراج مولفه p300 سیگنالهای eeg با روش موجک در سیستم bci، مبتنی بر p300 speller paradigm
چکیده ندارد.
15 صفحه اولEnsemble SWLDA Classifiers for the P300 Speller
The P300 Speller has proven to be an effective paradigm for braincomputer interface (BCI) communication. Using this paradigm, studies have shown that a simple linear classifier can perform as well as more complex nonlinear classifiers. Several studies have examined methods such as Fisher’s Linear Discriminant (FLD), Stepwise Linear Discriminant Analysis (SWLDA), and Support Vector Machines (SVM...
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ژورنال
عنوان ژورنال: Computational and Mathematical Methods in Medicine
سال: 2018
ISSN: 1748-670X,1748-6718
DOI: 10.1155/2018/4089021