Performance Evaluation of a Simple Deep Neural Network System for Auditory Attention Decoding

Recent years have seen an increase in the variety of methods used to perform Auditory Attention Decoding (AAD). Current high performing methods for auditory attention decoding rely on large training sets and lack comparable standards with one another largely due to the variability in the training data used. Simple standards between these models could help researchers better interpret performance and direct the progression of work to a model that performs the best. Here the performance of a Deep Neural Network (DNN) architecture for AAD proposed by (Cicarelli et al.,2019) is evaluated on a new, smaller set of training data collected in (Fuglsang et al.,2017). The network is shown to successfully achieve learning behavior when presented with the reduction of training data. Limiting the number of listeners used for training based on the output average loss curve resulted in comparable decoding accuracy. Further metrics show the benefit of an analysis of the relevance of listeners used for training of the network. The consistent performance of the network given the reduction in the provided training data shows how the simple DNN is a robust method for performing AAD. It also allows us to properly compare the performance of the DNN with the linear method in (Fuglsang et al.,2017)

A Tutorial on Auditory Attention Identification Methods

Auditory attention identification methods attempt to identify the sound source of a listener's interest by analyzing measurements of electrophysiological data. We present a tutorial on the numerous techniques that have been developed in recent decades, and we present an overview of current trends in multivariate correlation-based and model-based learning frameworks. The focus is on the use of linear relations between electrophysiological and audio data. The way in which these relations are computed differs. For example, canonical correlation analysis (CCA) finds a linear subset of electrophysiological data that best correlates to audio data and a similar subset of audio data that best correlates to electrophysiological data. Model-based (encoding and decoding) approaches focus on either of these two sets. We investigate the similarities and differences between these linear model philosophies. We focus on (1) correlation-based approaches (CCA), (2) encoding/decoding models based on dense estimation, and (3) (adaptive) encoding/decoding models based on sparse estimation. The specific focus is on sparsity-driven adaptive encoding models and comparing the methodology in state-of-the-art models found in the auditory literature. Furthermore, we outline the main signal processing pipeline for how to identify the attended sound source in a cocktail party environment from the raw electrophysiological data with all the necessary steps, complemented with the necessary MATLAB code and the relevant references for each step. Our main aim is to compare the methodology of the available methods, and provide numerical illustrations to some of them to get a feeling for their potential. A thorough performance comparison is outside the scope of this tutorial

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

HCI researchers interest in BCI is increasing because the technology industry is expanding into application areas where efficiency is not the main goal of concern. Domestic or public space use of information and communication technology raise awareness of the importance of affect, comfort, family, community, or playfulness, rather than efficiency. Therefore, in addition to non-clinical BCI applications that require efficiency and precision, this Research Topic also addresses the use of BCI for various types of domestic, entertainment, educational, sports, and well-being applications. These applications can relate to an individual user as well as to multiple cooperating or competing users. We also see a renewed interest of artists to make use of such devices to design interactive art installations that know about the brain activity of an individual user or the collective brain activity of a group of users, for example, an audience. Hence, this Research Topic also addresses how BCI technology influences artistic creation and practice, and the use of BCI technology to manipulate and control sound, video, and virtual and augmented reality (VR/AR)