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BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.

Yijun Wang·Zhiguang Zhang·Yong Li·Xiaorong Gao·Shangkai Gao·Fusheng Yang
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Abstract

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set (self-paced 1s) of "BCI Competition 2003" with a classification accuracy of 84% on the test set.

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AlgorithmsArtificial IntelligenceCerebral CortexCognitionComputer PeripheralsDatabases, FactualDiscriminant AnalysisElectroencephalographyEvoked Potentials, MotorFingersHumansImaginationModels, NeurologicalMotor CortexMovementPattern Recognition, AutomatedPrincipal Component AnalysisReproducibility of ResultsSensitivity and SpecificitySignal Processing, Computer-AssistedSomatosensory CortexUser-Computer InterfaceComparative StudyEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov'tValidation Study

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Yijun Wang, Zhiguang Zhang, Yong Li, Xiaorong Gao, Shangkai Gao, Fusheng Yang. 2004. BCI Competition 2003--Data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG.. https://doi.org/10.1109/tbme.2004.826697

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