Journal of Neural Engineering · 2005 · 534 citations · 12 references
EngineeringElectrical EngineeringNeuroscienceElectrophysiologySensory NeuroscienceVisual NeuroscienceBrain-computer InterfacesElectroencephalographyEvoked PotentialsNeural InterfacesNeural Signal ProcessingVisual Evoked PotentialsClassification AccuracyClassification Accuracy AnalysisClassification Accuracy AssessmentClassification Algorithms For Brain-computer InterfacesElectroencephalography-based Brain-computer InterfacesHarmonic Analysis Of ElectroencephalographyHarmonicsVisually Evoked Potentials
Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.
12
Neural networks for pattern recognition
Choice Reviews Online · 1994 · 18.7K citations
Motor imagery and direct brain-computer communication
G. Pfurtscheller, Christa Neuper · Proceedings of the IEEE · 2001 · 1.9K citations
Motor imagery activates primary sensorimotor area in humans
Gert Pfurtscheller, Christa Neuper · Neuroscience Letters · 1997 · 1.1K citations