Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces

Institute of Automation

PubMed
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Abstract

A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for…

Citation impact

780
total citations
FWCI
7.53
Percentile
100%
References
56
Citations per year

Authors

2

Topics & keywords

Keywords
  • Brain–computer interface
  • Computer science
  • Convolutional neural network
  • Oddball paradigm
  • Artificial intelligence
  • Pattern recognition (psychology)
  • Interface (matter)
  • Electroencephalography
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