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Signal models for brain interfaces based on evoked response potential in EEG

Signal models for brain interfaces based on evoked response potential in EEG

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Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are developed to provide access channels for alternative communication and control systems to people with severe speech and physical impairments. Designs that exploit evoked response potentials (ERPs) in EEG constitute the majority of research efforts dedicated to noninvasive BCIs. Visual, auditory, and tactile stimulation paradigms are used to actively probe the user's brain to collect EEG evidence towards inferring intent in the context of the particular application. As assistive technology devices, however, existing EEG-based BCIs lack sufficient speed and accuracy to safely and reliably restore function at acceptable levels. This is mainly because the recorded EEG signals are not only noisy with a low signal-to-noise ratio, but are also nonstationary, due to physiological or environmental artifacts, sensor failure, and user fatigue. In this chapter, we address how reliable intent inference engines with reasonable speed and accuracy can be developed using parametric modeling. Examples of real-world data in the framework of the ERP-based BCI paradigm are provided to exemplify our detection and classification methods.

Chapter Contents:

  • Abstract
  • 10.1 ERP-based BCIs
  • 10.1.1 Multidimensional EEG classification
  • 10.1.2 Nonstationarities in EEG signals
  • 10.1.3 Noise in the class labels
  • 10.2 ERP-based inference
  • 10.2.1 ERP detection
  • 10.2.2 Linear model and covariance matrix structures
  • 10.2.2.1 Imposing a spatial characteristics on the brain sources
  • 10.2.2.2 Imposing a temporal characteristics on the brain sources
  • 10.2.3 Nonstationarities detection
  • 10.2.4 Decoupling the class label from ERP detection
  • 10.3 Experimental results and discussions
  • 10.3.1 ERP-based BCI typing system
  • 10.3.1.1 BCI performance under spatial–temporal structures
  • 10.3.1.2 BCI performance under nonstationarities in EEG signals
  • 10.3.2 ERP-based BCI with tactile stimuli
  • 10.3.2.1 Classification performance under noisy labels
  • 10.4 Summary
  • References

Inspec keywords: handicapped aids; electroencephalography; medical signal processing; signal classification; brain-computer interfaces; brain

Other keywords: ERP-based BCI paradigm; user fatigue; auditory, stimulation paradigms; noninvasive BCIs; particular application; severe speech; low signal-to-noise ratio; tactile stimulation paradigms; signal models; response potentials; reliable intent inference engines; brain interfaces; visual stimulation paradigms; electroencephalography-based brain-computer interfaces; parametric modeling; control systems; alternative communication; access channels; physical impairments; evoked response potential; assistive technology devices; recorded EEG signals; reasonable speed

Subjects: User interfaces; Knowledge engineering techniques; Biology and medical computing; Probability theory, stochastic processes, and statistics; Electrodiagnostics and other electrical measurement techniques; Signal processing and detection; Digital signal processing; Electrical activity in neurophysiological processes; Bioelectric signals

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