Computational Intelligence and Neuroscience · 2011 · 108 citations · 15 references
ComputingComputer ScienceNeuroscienceFeature EngineeringMachine LearningMotor ControlBrain-computer InterfacesElectroencephalogram AnalysisElectroencephalographyMental ImageryMulti-task LearningPattern RecognitionBrain-computer Interface ClassificationCalibration Methods For Brain-computer InterfacesClassification Algorithms For Brain-computer InterfacesMotor Imagery AnalysisMotor Imagery ClassificationMultitask LearningNeural Data PreprocessingSpatial Filter Design
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 new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.
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