An optimization model for electroencephalography-assisted binaural beamforming

In a multi-speakers noisy environment, the incoherent correlation between the electroencephalography (EEG) signal and the attended speech envelope provides a user-informed way to design the beamformer for preserving the attended speech and suppressing others. In this work, we exploit such incoherent correlation property in terms of Pearson correlation and propose a unified optimization model for simultaneously designing beamformer and aligning attention preference of the user to each speech source. To balance different design considerations, the proposed optimization formulation makes a trade-off among aligning attention preference, controlling speech distortion, and reducing noise in a weighted manner in the objective function. Specifically, the attention preference is aligned by maximizing the Pearson correlation between the envelope of beamforming output and the linearly transformed EEG signal. To control speech distortion with flexibility, the spatial response of the beamformer to each source is penal...