  Noninvasive EEG (electroencephalography) based auditory attention detection
could be useful for improved hearing aids in the future. This work is a novel
attempt to investigate the feasibility of online modulation of sound sources by
probabilistic detection of auditory attention, using a noninvasive EEG-based
brain computer interface. Proposed online system modulates the upcoming sound
sources through gain adaptation which employs probabilistic decisions (soft
decisions) from a classifier trained on offline calibration data. In this work,
calibration EEG data were collected in sessions where the participants listened
to two sound sources (one attended and one unattended). Cross-correlation
coefficients between the EEG measurements and the attended and unattended sound
source envelope (estimates) are used to show differences in sharpness and
delays of neural responses for attended versus unattended sound source. Salient
features to distinguish attended sources from the unattended ones in the
correlation patterns have been identified, and later they have been used to
train an auditory attention classifier. Compared to the existing results in the
literature, in this paper we have two main contributions. First, using the
auditory attention classifier, we have shown high offline detection performance
with single channel EEG measurements of shorter duration compared to the
existing approaches in the literature which employ large number of channels
with longer EEG measurements. Second, using the classifier trained offline in
the calibration session, we have shown the performance of the online sound
source modulation system. We observe that online sound source modulation system
is able to keep the level of attended sound source higher than the unattended
source.
  Keywords: auditory BCI, cocktail party problem, auditory attention
classification
