Abstract

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

Highlights

  • Brain-computer interfaces (BCIs) can provide a communication channel without the involvement of muscular activity [1,2]

  • As we found during previous research, State VisuallyEvoked Potential (SSVEP) systems with four or less targets allow high classification rates and offer control to a wide range of users [7]

  • To obtain information transfer rates (ITRs) in bits per minute, B is multiplied by the number of command classifications per minute

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Summary

Introduction

Brain-computer interfaces (BCIs) can provide a communication channel without the involvement of muscular activity [1,2]. Through the detection of specific brain patterns, in the noninvasively acquired electroencephalogram (EEG) data, users are enabled to perform direct commands in real time. BCIs have the potential to be utilized as assistive technology for people with restricted motor abilities. In this article we present a communication system that is based on the Steady-State Visually. Evoked Potential (SSVEP) BCI paradigm [3,4,5]. SSVEP-based BCIs can be categorized as reactive BCI paradigm as it is based on the response to an external stimuli. Potentials are evoked at a certain frequency if the gaze is fixated on a flickering target at the same frequency

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