Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification

Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form separate information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for future use in a brain-computer interface. In a musical oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips, with each instrument occasionally playing one or several notes deviating from an otherwise repetitive pattern. Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain-computer interface and music research.