Effect of Distracting Background Speech in an Auditory Brain-Computer Interface

Abstract

Studies so far have analyzed the effect of distractor stimuli in different types of brain-computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.

Keywords: auditory; brain–computer interface (BCI); distractor; event-related potential (ERP); workload.

Figure 1

Trial time of a run. The number of sequences was 8 for the offline sessions and variable for the online sessions. The stimuli are R1000 (1000 Hz right), R200 (200 Hz right), L1000 (1000 Hz left), and L200 (200 Hz left). In this illustration, the target stimulus is presented in brown (200 Hz Left in this example). The feedback was only provided in the online sessions.

Figure 2

Experimental protocol. Each condition consisted of three calibration and two online blocks. Each block consisted of 8 runs (2 min 50 s for the calibration sessions, and shorter for the online sessions). The attentional verification questionnaires were provided at the end of each block only for conditions C2 and C3.

Figure 3

Accuracy (%; mean ± standard error) of each condition (C1, no speech; C2, ignoring the speech; C3, listening to the speech) as a function of the number of sequences during the calibration phase.

Figure 4

(A) Accuracy (%; mean ± standard error) and (B)ITR (bits/min; mean ± standard error) of each condition (C1, no speech; C2, ignoring the speech; C3, listening to the speech) during the online phase.

Figure 5

(A) Grand average event-related potential (ERP) waveform (microvolts) for target stimuli in Cz and Pz, for the three conditions (C1, no speech; C2, ignoring the speech; C3, listening to the speech). (B) Topographical scalp map for the P300 component (450–550 ms) of each condition. Both plots have been obtained from the calibration phase.

Figure 6

Scores (mean ± standard error) of total workload and unweighted six subdimensions for each condition (C1, no speech; C2, ignoring the speech; C3, attending to the speech) for NASA-TLX.