Biology subjects

Wong, D. D. E.

Publications and source records attributed to Wong, D. D. E..

3 recordsLinked to original sources

bioRxiv2024

Real-time control of a hearing instrument with EEG-based attention decoding

Enhancing speech perception in everyday noisy acoustic environments remains an outstanding challenge for hearing aids. Speech separation technology is improving rapidly, but hearing devices cannot fully exploit this advance without knowing which sound sources the user wants to hear. Even with high-quality source separation, the hearing aid must know which speech streams to enhance and which to suppress. Advances in EEG-based decoding of auditory attention raise the potential of neurosteering, in which a hearing instrument selectively enhances the sound sources that a hearing-impaired listener is focusing their attention on. Here, we present and discuss a real-time brain-computer interface (BCI) system that combines a stimulus-response model based on canonical correlation analysis (CCA) for real-time EEG attention decoding, coupled with a multi-microphone hardware platform enabling low-latency real-time speech separation through spatial beamforming. We provide an overview of the system and its various components, discuss prospects and limitations of the technology, and illustrate its application with case studies of listeners steering acoustic feedback of competing speech streams via real-time attention decoding. A software implementation code of the system is publicly available for further research and explorations.

neuroscience↗

bioRxiv2018

Accurate Modeling of Brain Responses to Speech

Perceptual processes can be probed by fitting stimulus-response models that relate measured brain signals such as electroencephalography (EEG) to the stimuli that evoke them. These models have also found application for the control of devices such as hearing aids. The quality of the fit, as measured by correlation, classification, or information rate metrics, indicates the value of the model and the usefulness of the device. Models based on Canonical Correlation Analysis (CCA) achieve a quality of fit that surpasses that of commonly-used linear forward and backward models. Here, we show that their performance can be further improved using several techniques that capture the time-varying and context-dependent relationships within the data, including adaptive beamforming, CCA weight optimization, and recurrent neural networks that capture the time-varying and context-dependent relationships within the data. We demonstrate these results using a match-vs-mismatch classification paradigm, in which the classifier must decide which of two stimulus samples produced a given EEG response and which is a randomly chosen stimulus sample. This task captures the essential features of the more complex auditory attention decoding (AAD) task explored in many other studies.

neuroscience↗

bioRxiv2018

A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on temporal response functions (TRFs). In the current context, a TRF is a function that facilitates a mapping between features of sound streams and EEG responses. It has been shown that when the envelope of attended speech and EEG responses are used to derive TRF mapping functions, the TRF model predictions can be used to discriminate between attended and unattended talkers. However, the predictive performance of the TRF models is dependent on how the TRF model parameters are estimated. There exist a number of TRF estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different TRF estimation methods to classify attended speakers from multi-channel EEG data. The performance of the TRF estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams.

neuroscience↗