Implementing selective attention using canonical correlation analysis-based auditory steady state response

Many researchers are concerned with the analysis of human intention, which is based on decision-making. However, understanding someone´s intentions can sometimes be difficult because it is related to many patterns that are dependent on individual situations. In order to analyze intention, we used a brain-mapping system to study the human brain. We call this the brain-computer interface (BCI) system. Earlier studies of BCI were based on visual stimuli. Using auditory stimuli, however, is still in its infancy in the field of research. In this paper, we proposed an auditory paradigm using the auditory steady-state response (ASSR) and canonical correlation analysis (CCA) to improve the accuracy of selective attention. It was made by our own experimental environment using the event-related potentials as P300. We describe a paradigm that allows a user to make a decision based on intention. We studied an auditory paradigm-based brain computer interfaces for intention recognition and how we can use it in BCI systems.