Multiband Common Spatial Pattern based EEG Classification for Brain-Computer Interface
نویسندگان
چکیده
منابع مشابه
Multiband Common Spatial Pattern based EEG Classification for Brain-Computer Interface
Thispaper presents a novel method for electroencephalography (EEG) based motor imagery classification for brain computer interface (BCI) implementation using the potential features extracted bandspecific common spatial pattern (CSP). The recorded EEG signal is bandpass-filtered into multiple subbands to capture the related rhythmic components of brain signals. The CSP features are then extracte...
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Common spatial pattern (CSP) method is highly successful in calculating spatial filters for motor imagery-based brain-computer interfaces (BCIs). However, conventional CSP algorithm is based on a single wide frequency band with a poor frequency selectivity which will lead to poor recognition accuracy. To solve this problem, a novel Partitioned CSP (PCSP) algorithm is proposed to find the most r...
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In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts. Common Spatial Pattern (CSP) algorithm has been proved to be effective in BCI for extracting features in motor imagery tasks, but it is prone to overfitting. Many algorithms have been devised to regularize CSP for two class problem, ...
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Common spatial pattern (CSP) algorithm and principal component analysis (PCA) are two commonly used key techniques for EEG component selection and EEG feature extraction for EEG-based braincomputer interfaces (BCIs). However, both the ordinary CSP and PCA algorithms face a loading problem, i.e., their weights in linear combinations are non-zero. This problem makes a BCI system easy to be over-f...
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ژورنال
عنوان ژورنال: IOSR Journal of Computer Engineering
سال: 2017
ISSN: 2278-8727,2278-0661
DOI: 10.9790/0661-1902059099