Date Published: June 13, 2019
Publisher: Public Library of Science
Author(s): Felix Gembler, Piotr Stawicki, Abdul Saboor, Ivan Volosyak, Zhishun Wang.
Brain-Computer Interfaces (BCIs) based on visual evoked potentials (VEPs) allow high communication speeds and accuracies. The fastest speeds can be achieved if targets are identified in a synchronous way (i.e., after a pre-set time period the system will produce a command output). The duration a target needs to be fixated on until the system classifies an output command affects the overall system performance. Hence, extracting a data window dedicated for the classification is of critical importance for VEP-based BCIs. Secondly, unintentional fixation on a target could easily lead to its selection. For the practical usability of BCI applications it is desirable to distinguish between intentional and unintentional fixations. This can be achieved by using threshold-based target identification methods. The study explores personalized dynamic classification time windows for threshold-based time synchronous VEP BCIs. The proposed techniques were tested employing the SSVEP and the c-VEP paradigm. Spelling performance was evaluated using an 8-target dictionary-supported BCI utilizing an n-gram word prediction model. The performance of twelve healthy participants was assessed with the information transfer rate (ITR) and accuracy. All participants completed sentence spelling tasks, reaching average accuracies of 94% and 96.3% for the c-VEP and the SSVEP paradigm, respectively. Average ITRs around 57 bpm were achieved for both paradigms.
Brain-Computer Interfaces (BCIs) detect, analyze, and decode brain activities to provide communication with the external environment, without involving any muscle activities . The brain activities are usually recorded non-invasively by an electroencephalogram (EEG). BCIs may be used as a communication tool for severely impaired people [2, 3].
The main purpose of the presented study was to investigate methods of dynamic gaze classification time windows for time synchronous VEP BCIs. The proposed methods lead to a more natural user-BCI interaction. To demonstrate the robustness of the approach, a dictionary-driven spelling application was tested with the SSVEP and the c-VEP paradigm. In this sense, the study also provides a direct comparison between c-VEP and SSVEP stimulation, both in terms of performance and user-friendliness.