Detection of command intentions by voice and EEG using FTM

Abstract

Voice recognition technology allows for the control of external devices without the need for physical movements. However, it can sometimes misinterpret ambient sounds or respond to unintentionally spoken startup commands. Triggering action not intended by the user. Therefore, research is underway to decern the“ user’s intention ”at the time of voice command activation. In this study, we attempt to identify user intentions using Brain-Computer Interface (BCI), which learns from voice and EEG data though a Learning-type Fuzzy Template Matching(L-FTM) method. The L-FTM method employs linguistic labels such as ’High’ and ’Low’ instead of actual values to ambiguously represent the characteristics of the input data. This approach allows the L-FTM method to learn the features of input data while suppressing the effects of noise in data such as EEG, which is known to have significant individual differences, and speech data that includes environmental sounds and noises. Participants were seated in a quiet room, in front of a desk wearing a head cap connected to an electroencephalograph. The desk was equipped with a PC for stimulus presentation and audio acquisition, and a Smart Speaker. During the leaning phases, buttons labeled,“ with intention ”and“ without intention ”were displayed on the PC screen. Participants were asked to vocalize “ Alexa ” after selecting either button, and the vocalizations with and without intention were recorded 30 times each. When determining the presence or absence of command intention, participants were asked to perform the same task as during the learning phase, with six vocalizations of “ with intention ” and six vocalizations of “ without intention ” each, Subsequently participants were asked to identify whether these vocalizations were intentional or not.