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.

Current brain-computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min(-1) and of 26.56 ± 9.224 bit min(-1), respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications.

A new multiclass brain-computer interface (BCI) based on the modulation of sensorimotor oscillations by imagining movements is described. By the application of advanced signal processing tools, statistics and machine learning, this BCI system offers: 1) asynchronous mode of operation, 2) automatic selection of user-dependent parameters based on an initial calibration, 3) incremental update of the classifier parameters from feedback data. The signal classification uses spatially filtered signals and is based on spectral power estimation computed in individualized frequency bands, which are automatically identified by a specially tailored AR-based model. Relevant features are chosen by a criterion based on Mutual Information. Final recognition of motor imagery is effectuated by a multinomial logistic regression classifier. This BCI system was evaluated in two studies. In the first study, five participants trained the ability to imagine movements of the right hand, left hand and feet in response to visual cues. The accuracy of the classifier was evaluated across four training sessions with feedback. The second study assessed the information transfer rate (ITR) of the BCI in an asynchronous application. The subjects' task was to navigate a cursor along a computer rendered 2-D maze. A peak information transfer rate of 8.0 bit/min was achieved. Five subjects performed with a mean ITR of 4.5 bit/min and an accuracy of 74.84%. These results demonstrate that the use of automated interfaces to reduce complexity for the intended operator (outside the laboratory) is indeed possible. The signal processing and classifier source code embedded in BCI2000 is available from https://www.brain-project.org/downloads.html.