Noninvasive EEG-Based Auditory Attention Detection for Online Modulation of Sound Sources
Keywords:
EEG, auditory attention, noninvasive, brain-computer interface, sound source modulationAbstract
Noninvasive EEG-based auditory attention detection has the potential to revolutionize the field of hearing aids. This study presents a novel investigation into the feasibility of online modulation of sound sources through probabilistic detection of auditory attention, utilizing a noninvasive EEG-based brain-computer interface. The proposed online system achieves modulation of upcoming sound sources by employing gain adaptation based on soft decisions from a classifier trained on offline calibration data. During calibration sessions, EEG data were collected while participants listened to two sound sources, one attended and one unattended. Cross-correlation coefficients between the EEG measurements and the envelope of the attended and unattended sound sources were analyzed to reveal differences in neural response sharpness and delays for attended versus unattended sound sources. Salient features were identified from the correlation patterns to distinguish attended sources from unattended ones and used to train an auditory attention classifier. The results of this study contribute significantly in two aspects. Firstly, the high offline detection performance of the auditory attention classifier was demonstrated using shorter duration single-channel EEG measurements, outperforming existing approaches that employ a larger number of channels and longer EEG recordings. Secondly, the performance of the online sound source modulation system, utilizing the classifier trained offline, was evaluated. The findings indicate that the online system effectively maintains a higher level of the attended sound source compared to the unattended source. This research paves the way for advancements in the development of improved hearing aids incorporating noninvasive EEG-based auditory attention detection.
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