منابع مشابه
Clustered Common Spatial Patterns
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...
متن کاملImproving Classification Performance of BCIs by Using Stationary Common Spatial Patterns and Unsupervised Bias Adaptation
Non-stationarities in EEG signals coming from electrode artefacts, muscular activity or changes of task involvement can negatively affect the classification accuracy of Brain-Computer Interface (BCI) systems. In this paper we investigate three methods to alleviate this: (1) Regularization of Common Spatial Patterns (CSP) towards stationary subspaces in order to reduce the influence of artefacts...
متن کاملCommon Spatial Patterns Feature Extraction and Support Vector Machine Classification for Motor Imagery with the SecondBrain
Recently, a large set of electroencephalography (EEG) data is being generated by several high-quality labs worldwide and is free to be used by all researchers in the world. On the other hand, many neuroscience researchers need these data to study different neural disorders for better diagnosis and evaluating the treatment. However, some format adaptation and pre-processing are necessary before ...
متن کاملMultisubject Learning for Common Spatial Patterns in Motor-Imagery BCI
Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a ne...
متن کاملA 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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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Systems and Rehabilitation Engineering
سال: 2019
ISSN: 1534-4320,1558-0210
DOI: 10.1109/tnsre.2019.2936411