How Many People Could Use an SSVEP BCI?

Abstract

Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a selected group of subjects. Also, many articles do not mention data from subjects who performed poorly. These and other factors have made it difficult to estimate how many people could use different BCIs. The present study explored how many subjects could use an SSVEP BCI. We recorded data from 53 subjects while they participated in 1–4 runs that were each 4 min long. During these runs, the subjects focused on one of four LEDs that each flickered at a different frequency. The eight channel EEG data were analyzed with a minimum energy parameter estimation algorithm and classified with linear discriminant analysis into one of the four classes. Online results showed that SSVEP BCIs could provide effective communication for all 53 subjects, resulting in a grand average accuracy of 95.5%. About 96.2% of the subjects reached an accuracy above 80%, and nobody was below 60%. This study showed that SSVEP based BCI systems can reach very high accuracies after only a very short training period. The SSVEP approach worked for all participating subjects, who attained accuracy well above chance level. This is important because it shows that SSVEP BCIs could provide communication for some users when other approaches might not work for them.

Performance of Brain–Computer Interfacing Based on Tactile Selective Sensation and Motor Imagery

“…As the number of experimental studies increased, it became evident that a portion of users cannot control specific BCI systems, even after extensive training [20]- [29], which has been termed "BCI Illiteracy" [26], [27]. Three major MI studies on 193 subjects [26], 80 subjects [30] and 52 subjects [31], have specifically investigated this problem.…”

A User-Friendly Dictionary-Supported SSVEP-based BCI Application

“…The dictionary driven speller was implemented as a four-class BCI-system as those systems allow the majority of users to gain control over the system [9,11,19]. Indeed, the accuracies achieved as well as the fact that all participants were able to control the system, confirm that a low number of simultaneously displayed targets might decrease the number of wrong selections, as discussed e.g.…”

“…However, it is known that low frequencies cause more fatigue [25]. A further disadvantage of those frequencies that they overlap with the alpha band (8)(9)(10)(11)(12)(13). If the participant closed the eyes a little too long false classifications might occur.…”

“…Four class SSVEP-based BCIs have been proven to grant sufficient control over the system and allow greater freedom in the choice of stimulation frequencies than systems with multiple targets [9,11].…”

Statistical evaluation of a novel SSVEP-BCI stimulation setup based on depth-of-field

“…BCI systems based on SSVEP (or SSVEP-BCI) are becoming robust systems because their response has high signal-noise rate (SNR) and are achieving high transfer rates (Allison et al, 2010;Guger et al, 2012). SSVEP-BCI systems are being employed in human-machine interaction for assistance to people with disabilities Muller et al, 2010), spellers (Yin et al, 2015) and games.…”