Probabilistic Common Spatial Patterns for Multichannel EEG Analysis
نویسندگان
چکیده
منابع مشابه
A probabilistic framework for learning robust common spatial patterns Citation
Robustness in signal processing is crucial for the purpose of reliably interpreting physiological features from noisy data in biomedical applications. We present a robust algorithm based on the reformulation of a well-known spatial filtering and feature extraction algorithm named Common Spatial Patterns (CSP). We cast the problem of learning CSP into a probabilistic framework, which allows us t...
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We propose to cluster class-wise covariance matrices in order to identify different groups of covariances contributing to the same condition. Each cluster represents a different brain pattern associated with one class. Further, we present Clustered Common Spatial Patterns, a new algorithm that applies this technique prior to CSP. We show that CCSP can outperform CSP in a binary imagery movement...
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Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). ...
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BACKGROUND Brain computer interfaces (BCI) is one of the most popular branches in biomedical engineering. It aims at constructing a communication between the disabled persons and the auxiliary equipments in order to improve the patients' life. In motor imagery (MI) based BCI, one of the popular feature extraction strategies is Common Spatial Patterns (CSP). In practical BCI situation, scalp EEG...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2015
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2014.2330598