A Graphical Model for Online Auditory Scene Modulation Using EEG Evidence for Attention

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 25; no. 11; pp. 1970 - 1977
• Main Authors: Haghighi, Marzieh, Moghadamfalahi, Mohammad, Akcakaya, Murat, Shinn-Cunningham, Barbara G., Erdogmus, Deniz
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Attention; Attention - physiology; auditory attention detection; Auditory Perception - physiology; Brain; brain interface; Brain modeling; Brain-Computer Interfaces; Calibration; Conditional probability; Context modeling; EEG; Electroencephalography; Energy consumption; Female; Graphical models; Hearing aids; Humans; Interfaces; Male; Models, Neurological; Modulation; Online Systems; Probability distribution; Prosthesis Design; Scene analysis; Signal Processing, Computer-Assisted; Speech; Speech Perception; Transfer, Psychology; Virtual environments; Waveforms; Wavelet Analysis
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2017.2712419

Recent findings indicate that brain interfaces have the potential to enable attention-guided auditory scene analysis and manipulation in applications, such as hearing aids and augmented/virtual environments. Specifically, noninvasively acquired electroencephalography (EEG) signals have been demonstrated to carry some evidence regarding, which of multiple synchronous speech waveforms the subject attends to. In this paper, we demonstrate that: 1) using data- and model-driven cross-correlation features yield competitive binary auditory attention classification results with at most 20 s of EEG from 16 channels or even a single well-positioned channel; 2) a model calibrated using equal-energy speech waveforms competing for attention could perform well on estimating attention in closed-loop unbalanced-energy speech waveform situations, where the speech amplitudes are modulated by the estimated attention posterior probability distribution; 3) such a model would perform even better if it is corrected (linearly, in this instance) based on EEG evidence dependence on speech weights in the mixture; and 4) calibrating a model based on population EEG could result in acceptable performance for new individuals/users; therefore, EEG-based auditory attention classifiers may generalize across individuals, leading to reduced or eliminated calibration time and effort.