Wheelchair Control in a Virtual Environment by Healthy Participants Using a P300-BCI Based on Tactile Stimulation: Training Effects and Usability

Abstract

Tactile stimulation is less frequently used than visual for brain-computer interface (BCI) control, partly because of limitations in speed and accuracy. Non-visual BCI paradigms, however, may be required for patients who struggle with vision dependent BCIs because of a loss of gaze control. With the present study, we attempted to replicate earlier results by Herweg et al. (2016), with several minor adjustments and a focus on training effects and usability. We invited 16 healthy participants and trained them with a 4-class tactile P300-based BCI in five sessions. Their main task was to navigate a virtual wheelchair through a 3D apartment using the BCI. We found significant training effects on information transfer rate (ITR), which increased from a mean of 3.10-9.50 bits/min. Further, both online and offline accuracies significantly increased with training from 65% to 86% and 70% to 95%, respectively. We found only a descriptive increase of P300 amplitudes at Fz and Cz with training. Furthermore, we report subjective data from questionnaires, which indicated a relatively high workload and moderate to high satisfaction. Although our participants have not achieved the same high performance as in the Herweg et al. (2016) study, we provide evidence for training effects on performance with a tactile BCI and confirm the feasibility of the paradigm.

Keywords: P300; brain-computer interface (BCI); event-related-potential (ERP); replication; tactile; tactually evoked potentials; wheelchair control.

Figure 1

Two tactile actuators from the C2 tactor system by Engineering Acoustic Inc., Casselberry, FL, USA.

Figure 2

Overview of the virtual environment from the navigation task. (A) User’s perspective, showing the wheelchair and a checkpoint in front of it. (B) Floorplan of the apartment showing one of the courses. Adapted from Kaufmann et al. (2014).

Figure 3

Comparison of session 1 vs. 5 grand averages of target and non-target epochs to 800 ms post-stimulus. The highest target peak (6.21 μV) after training was observed at position Cz. The bar plot shows mean amplitudes from all sessions (error bars represent SE).

Figure 4

Comparison of target and non-target epochs at Cz from session 1 vs. 5, per participant.

Figure 5

Online brain-computer interface (BCI) performance from the navigation tasks. Data were averaged over all BCI efficient participants (error bars represent SE). Both information transfer rate (ITR; p = 0.002), online accuracy (p < 0.05) and offline accuracy (p < 0.01) increased significantly with training.