Towards estimating selective auditory attention from EEG using a novel time-frequency-synchronisation framework

An original experimental design is combined with a novel signal processing approach so as to provide cognitive clues in the study of auditory scene analysis and in the design of auditory brain computer interfaces. Volunteers attended a single auditory stimulus in a perceptually complex auditory environment of speech and music, wherein the experiment aim was to estimate the attended stimulus from recorded electroencephalogram (EEG). Unlike previous studies, the complex nature of the auditory environment does not allow for straightforward analysis that exploits convenient properties of the stimuli. To provide insight, synchronised neuronal activity was analysed within a novel signal processing framework that models energy and phase dynamics independently using empirical mode decomposition. By design, the proposed approach caters for higher order information and is suitable for nonstationary data, both critical properties in the analysis of cognitive activity. The proposed methodology achieved a median classification accuracy of 71% in a series of selective attention experiments with several volunteers.