DocumentCode
1474656
Title
BCI Demographics II: How Many (and What Kinds of) People Can Use a High-Frequency SSVEP BCI?
Author
Volosyak, Ivan ; Valbuena, Diana ; Lüth, Thorsten ; Malechka, Tatsiana ; Gräser, Axel
Author_Institution
Inst. of Autom., Univ. of Bremen, Bremen, Germany
Volume
19
Issue
3
fYear
2011
fDate
6/1/2011 12:00:00 AM
Firstpage
232
Lastpage
239
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.
Keywords
brain-computer interfaces; neurophysiology; visual evoked potentials; BCI demographics; Bremen-BCI system; brain activity; brain-computer interface; flickering stimuli; high-frequency SSVEP BCI; photosensitive epileptic seizure; steady-state visual evoked potential; user fatigue; Accuracy; Analysis of variance; Computers; Electrodes; Electroencephalography; Robot sensing systems; Brain–computer interface (BCI); demographics; electroencephalography (EEG); high-frequency visual stimulation; steady-state visual evoked potential (SSVEP); Adolescent; Adult; Age Factors; Algorithms; Brain; Computers; Demography; Electroencephalography; Evoked Potentials, Visual; Fatigue; Female; Humans; Information Theory; Male; Middle Aged; Photic Stimulation; Psychomotor Performance; Questionnaires; Robotics; Sex Factors; Signal Processing, Computer-Assisted; Software; User-Computer Interface; Video Games; Young Adult;
fLanguage
English
Journal_Title
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1534-4320
Type
jour
DOI
10.1109/TNSRE.2011.2121919
Filename
5733429
Link To Document