title: BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI?
abstract: Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.
authors.affiliation: Institute of Automation, University of Bremen, 28359 Bremen, Germany. volosyak@iat.uni-bremen.de
authors.forename: Ivan
authors.initials: I
authors.lastname: Volosyak
authors.forename: Diana
authors.initials: D
authors.lastname: Valbuena
authors.forename: Thorsten
authors.initials: T
authors.lastname: Lüth
authors.forename: Tatsiana
authors.initials: T
authors.lastname: Malechka
authors.forename: Axel
authors.initials: A
authors.lastname: Gräser
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003201:Computers; D003710:Demography; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005221:Fatigue / Q000523:psychology; D005260:Female; D006801:Humans; D007257:Information Theory; D008297:Male; D008875:Middle Aged; D010775:Photic Stimulation; D011597:Psychomotor Performance / Q000502:physiology; D012371:Robotics; D012737:Sex Factors; D012815:Signal Processing, Computer-Assisted; D012984:Software; D011795:Surveys and Questionnaires; D014584:User-Computer Interface; D018910:Video Games; D055815:Young Adult