A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI

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.

High-Speed Spelling in Virtual Reality with Sequential Hybrid BCIs

“…In a 30-target speller [8], a webcam-based eye tracking method was only able to detect binary gaze direction (left vs right). Another hybrid BCI obtained an information transfer rate (ITR) similar to SSVEP, but much lower than eye tracking [9]. According to high performance reported for eye tracking and SSVEP BCI [3], [5], the hybrid method is expected to achieve much higher performance than the existing systems.…”

“…Several recent studies have combined SSVEP and eye tracking to develop hybrid BCIs for spelling [8], [9]. However, due to performance gap between the two modalities, the hybrid systems did not achieve large performance improvement.…”

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

“…They also showed that an SSVEP-based BCI performed better than an eye tracking-based speller when targets are densely located and small in size. Recently, eye tracking has been combined with EEG to remove ocular artifacts from EEG signals [74,75] and to develop hybrid BCI systems [65,[76][77][78][79][80][81]. All these studies have shown improved performance as compared to EEG only, as well as the feasibility of combining EEG with eye tracking.…”

Towards Brain-Computer Interfaces for Drone Swarm Control

“…EEG-based BCI has several paradigms for signal acquisition such as motor imagery (MI) [6]- [8], event-related potential (ERP) [9], [10], and movement-related cortical potential (MRCP) [2], [11]. As applications of EEGbased BCI, a robotic arm [6], [12], [13], a speller [4], [14]- [16], a wheelchair [17], and a drone [18]- [21] were commonly used for communication between human and machines. Recently, a BCI-based drone swarm is one of the most interesting topics ( Fig.…”