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Square or Sine: Finding a Waveform with High Success Rate of Eliciting SSVEP
- Source :
- Computational Intelligence and Neuroscience, Vol 2011 (2011), Computational Intelligence and Neuroscience
- Publication Year :
- 2011
- Publisher :
- Hindawi Limited, 2011.
-
Abstract
- Steady state visual evoked potential (SSVEP) is the brain's natural electrical potential response for visual stimuli at specific frequencies. Using a visual stimulus flashing at some given frequency will entrain the SSVEP at the same frequency, thereby allowing determination of the subject's visual focus. The faster an SSVEP is identified, the higher information transmission rate the system achieves. Thus, an effective stimulus, defined as one with high success rate of eliciting SSVEP and high signal-noise ratio, is desired. Also, researchers observed that harmonic frequencies often appear in the SSVEP at a reduced magnitude. Are the harmonics in the SSVEP elicited by the fundamental stimulating frequency or by the artifacts of the stimuli? In this paper, we compare the SSVEP responses of three periodic stimuli: square wave (with different duty cycles), triangle wave, and sine wave to find an effective stimulus. We also demonstrate the connection between the strength of the harmonics in SSVEP and the type of stimulus.
- Subjects :
- Visual perception
Article Subject
General Computer Science
genetic structures
Computer science
General Mathematics
Speech recognition
Models, Neurological
Stimulus (physiology)
Visual system
lcsh:Computer applications to medicine. Medical informatics
lcsh:RC321-571
User-Computer Interface
Sine wave
medicine
Triangle wave
Waveform
Humans
Visual Pathways
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Visual Cortex
General Neuroscience
Electroencephalography
Signal Processing, Computer-Assisted
General Medicine
Electrophysiology
Visual cortex
medicine.anatomical_structure
Harmonics
Evoked Potentials, Visual
lcsh:R858-859.7
Algorithms
Photic Stimulation
Software
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 16875273 and 16875265
- Volume :
- 2011
- Database :
- OpenAIRE
- Journal :
- Computational Intelligence and Neuroscience
- Accession number :
- edsair.doi.dedup.....3e9a613b1aeb7120c67531b712b15dfa