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BCIs using steady-state visual-evoked potentials

BCIs using steady-state visual-evoked potentials

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Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuromuscular disabilities. Among the existing solutions, the systems relying on electroencephalograms (EEGs) occupy the most prominent place due to their noninvasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. In this chapter, we focus on the category of EEG-based BCIs that rely on steady-state-visual-evoked potentials (SSVEPs) and perform a comparative evaluation of the most promising algorithms existing in the literature. Moreover, we will also describe four novel approaches that are able to improve the accuracy of the interaction under different operational context.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Regression-based SSVEP recognition systems
  • 8.2.1 Multivariate linear regression (MLR) for SSVEP
  • 8.2.2 Sparse Bayesian LDA for SSVEP
  • 8.2.3 Kernel-based BLDA for SSVEP (linear kernel)
  • 8.2.4 Kernels for SSVEP
  • 8.2.4.1 CCA-based kernel
  • 8.2.4.2 PLS kernel
  • 8.2.5 Multiple kernel approach
  • 8.3 Results
  • 8.4 Summary
  • References

Inspec keywords: medical signal processing; visual evoked potentials; handicapped aids; electroencephalography; brain-computer interfaces

Other keywords: EEG-based BCIs; steady-state-visual-evoked potentials; neuromuscular disabilities; prominent place; brain-computer interfaces; computer commands; human-computer interaction; steady-state visual-evoked potentials

Subjects: Signal processing and detection; Electrodiagnostics and other electrical measurement techniques; Electrical activity in neurophysiological processes; Bioelectric signals; Biology and medical computing; Digital signal processing; User interfaces

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