Deep Correlation Analysis for Audio-EEG Decoding
This work addresses the challenge of improving audio-EEG decoding for researchers and clinicians by offering a novel deep learning approach, though it is incremental as it builds on existing correlation analysis methods.
The paper tackled the problem of EEG artifacts distorting stimulus-response analysis for auditory stimuli by proposing neural network-based correlation analysis frameworks, resulting in significant improvements over linear methods with average absolute gains of 7.4% in speech tasks and 29.3% in music tasks.
The electroencephalography (EEG), which is one of the easiest modes of recording brain activations in a non-invasive manner, is often distorted due to recording artifacts which adversely impacts the stimulus-response analysis. The most prominent techniques thus far attempt to improve the stimulus-response correlations using linear methods. In this paper, we propose a neural network based correlation analysis framework that significantly improves over the linear methods for auditory stimuli. A deep model is proposed for intra-subject audio-EEG analysis based on directly optimizing the correlation loss. Further, a neural network model with a shared encoder architecture is proposed for improving the inter-subject stimulus response correlations. These models attempt to suppress the EEG artifacts while preserving the components related to the stimulus. Several experiments are performed using EEG recordings from subjects listening to speech and music stimuli. In these experiments, we show that the deep models improve the Pearson correlation significantly over the linear methods (average absolute improvements of 7.4% in speech tasks and 29.3% in music tasks). We also analyze the impact of several model parameters on the stimulus-response correlation.