Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
Presence: Teleoperators and Virtual Environments
Presence: Teleoperators and Virtual Environments
EEG signals classification for brain computer interfaces based on Gaussian process classifier
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Classification of electroencephalogram signals with combined time and frequency features
Expert Systems with Applications: An International Journal
Applying evolution strategies to preprocessing EEG signals for brain-computer interfaces
Information Sciences: an International Journal
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Classifying electroencephalography (EEG) signals is an important step for proceeding EEG-based brain computer interfaces (BCI). Currently, kernel based methods such as the support vector machine (SVM) are considered the state-of-the-art methods for this problem. In this paper, we apply Gaussian process (GP) classification to binary discrimination of motor imagery EEG data. Compared with the SVM, GP based methods naturally provide probability outputs for identifying a trusted prediction which can be used for post-processing in a BCI. Experimental results show that the classification methods based on a GP perform similarly to kernel logistic regression and probabilistic SVM in terms of predictive likelihood, but outperform SVM and K-nearest neighbor (KNN) in terms of 0-1 loss class prediction error.