Deep Canonical Correlation Analysis For Decoding The Auditory Brain

The process of decoding the auditory brain for an acoustic stimulus involves finding the relationship between the audio input and the brain activity measured in terms of Electroencephalography (EEG) recordings. Prior methods focus on linear analysis methods like Canonical Correlation Analysis (CCA) to establish a relationship. In this paper, we present a deep learning framework that is learned to maximize correlation. For dealing with high levels of noise in EEG data, we employ regularization techniques and experiment with various model architectures. With a paired dataset of audio envelope and EEG, we perform several experiments with deep correlation analysis using forward and backward correlation models. In these experiments, we show that regularized deep CCA is consistently able to outperform the linear models in terms of providing improved correlation (up to 9% absolute improvement in Pearson correlation which is statistically significant). We present an analysis that highlights the benefits of using dropouts for neural network regularization in the deep CCA model.Clinical relevance - The proposed method helps to decode human auditory attention. In the case of overlapping speech from two speakers, decoding the auditory attention provides information about how well the sources are separated in the brain and which of the sources is attended. This can impact cochlear implants that use EEG for decoding attention as well as in development of BCI applications. The correlation method proposed in this work can also be extended to other modalities like visual stimuli.