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Article type: Research Article
Authors: Wilson, John J.a | Palaniappan, Ramaswamyb; *
Affiliations: [a] School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK | [b] Department of Engineering, Faculty of Science and Engineering, University of Wolverhampton, Telford, UK
Correspondence: [*] Corresponding author: Ramaswamy Palaniappan, Room SC035, Department of Engineering Faculty of Science and Engineering University of Wolverhampton Shifnal Road, Priorslee TF2 9NT, Telford, UK. Tel.: +44 0 1902 323 875; Fax: +44 0 1902 323 843; E-mail: [email protected]
Abstract: Brain-computer interfaces (BCI) are useful devices that allow direct control of external devices using thoughts, i.e. brain's electrical activity. There are several BCI paradigms, of which steady state visual evoked potential (SSVEP) is the most commonly used due to its quick response and accuracy. SSVEP stimuli are typically generated by varying the luminance of a target for a set number of frames or display events. Conventionally, SSVEP based BCI paradigms use magnitude (amplitude) information from frequency domain but recently, SSVEP based BCI paradigms have begun to utilize phase information to discriminate between similar frequency targets. This paper will demonstrate that using a single frame to modulate a stimulus may lead to a bi-modal distribution of SSVEP as a consequence of a user attending both transition edges. This incoherence, while of less importance in traditional magnitude domain SSVEP BCIs becomes critical when phase is taken into account. An alternative modulation technique incorporating a 50% duty cycle is also a popular method for generating SSVEP stimuli but has a unimodal distribution due to user's forced attention to a single transition edge. This paper demonstrates that utilizing the second method results in significantly enhanced performance in information transfer rate in a phase discrimination SSVEP based BCI.
Keywords: BCI, duty cycle, SSVEP, phase information
DOI: 10.3233/KES-140287
Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 18, no. 2, pp. 73-79, 2014
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