Effects of spectral smearing of stimuli on the performance of auditory steady-state response-based brain-computer interface

Abstract

There have been few reports that investigated the effects of the degree and pattern of a spectral smearing of stimuli due to deteriorated hearing ability on the performance of auditory brain-computer interface (BCI) systems. In this study, we assumed that such spectral smearing of stimuli may affect the performance of an auditory steady-state response (ASSR)-based BCI system and performed subjective experiments using 10 normal-hearing subjects to verify this assumption. We constructed smearing-reflected stimuli using an 8-channel vocoder with moderate and severe hearing loss setups and, using these stimuli, performed subjective concentration tests with three symmetric and six asymmetric smearing patterns while recording electroencephalogram signals. Then, 56 ratio features were calculated from the recorded signals, and the accuracies of the BCI selections were calculated and compared. Experimental results demonstrated that (1) applying smearing-reflected stimuli decreases the performance of an ASSR-based auditory BCI system, and (2) such negative effects can be reduced by adjusting the feature settings of the BCI algorithm on the basis of results acquired a posteriori. These results imply that by fine-tuning the feature settings of the BCI algorithm according to the degree and pattern of hearing ability deterioration of the recipient, the clinical benefits of a BCI system can be improved.

Keywords: Brain–computer interface; Hearing impairment; Spectral smearing; Subjective experiment.

Fig. 1

Scheme and characteristics of the implemented 8-channel vocoder. a Block diagram; b ROEX filter characteristics in the ML and HL setup; c output of the vocoder for S₅₀₀{37} in the ML and HL setup; d output of the vocoder for S₂₀₀₀{43} in the ML and HL setup

Fig. 2

Waveforms of stimuli with no, mid-level, and high-level spectral smearing. a S₅₀₀{37}_NL; b S₂₀₀₀{43}_NL; c S₅₀₀{37}_ML; d S₂₀₀₀{43}_ML; e S₅₀₀{37}_HL; f S₂₀₀₀{43}_HL

Fig. 3

Results of non-parametric Mann–Whitney test (two-sided test) for the BCI accuracies of the cross validation in fixed-feature cases. a SYM test; b ASYM test

Fig. 4

Results of non-parametric Mann–Whitney test (two-sided test) for the BCI accuracies of the cross validation in adjusted-feature cases. a SYM test; b ASYM test; *indicates a significant difference (p < 0.05)

Fig. 5

Results of non-parametric Mann–Whitney test (two-sided test) for the BCI accuracies of the cross validation in recommended-feature cases. a SYM test; b ASYM test; *indicates a significant difference (p < 0.05)

Fig. 6

Results of comparisons between fixed-feature, adjusted-feature, and recommended-feature cases. a SYM test; b ASYM test