Classification of auditory steady-state responses to speech data

This paper presents an auditory steady-state response (ASSR)-based brain-computer interface (BCI) that uses artificial speech data synthesized by a text-to-speech (TTS) system. Many ASSR-based BCI systems that use pure tone (sinusoid) or an abrupt beep as auditory stimuli have been proposed. However, while these systems have achieved high classification accuracy, our group has found that participants find the monotonous stimuli to be hypnotic and annoying. Practical BCI systems should have user-friendly designs. Thus, as a first step, we develop a new experimental BCI system in which we change the type of stimuli from pure tone carrier to artificial speech data, which are clear enough for participants to recognize the meaning of sentences. With eight participants, the average accuracy of the system is 78.6 ± 5.32% for the binary classification problem. This suggests that the proposed system can be used in practical BCI.

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