Biology subjects

Slaney, M.

Publications and source records attributed to Slaney, M..

5 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↗

bioRxiv2020

Auditory Stimulus-response Modeling with a Match-Mismatch Task

An auditory stimulus can be related to the brain response that it evokes by a stimulus-response model fit to the data. This offers insight into perceptual processes within the brain and is also of potential use for devices such as Brain Computer Interfaces (BCI). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance. Here we focus on a match-mismatch (MM) task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it. The MM task allows stimulus-response models to be evaluated in the limit of very high model accuracy, making it an attractive alternative to the more commonly used task of auditory attention detection (AAD). The MM task does not require class labels, so it is immune to mislabeling, and it is applicable to data recorded in listening scenarios with only one sound source, thus it is cheap to obtain large quantities of training and testing data. Performance metrics from this task, associated with regression accuracy, provide complementary insights into the relation between stimulus and response, as well as information about discriminatory power directly applicable to BCI applications. Using these metrics, we describe a range of models of increasing complexity that we compare to methods in the literature, showing state-of-the-art performance. We document in detail one particular implementation, calibrated on a publicly-available database, that can serve as a robust reference to evaluate future developments.

neuroscience↗

bioRxiv2018

Rapid ocular responses are a robust marker for bottom-up driven auditory salience

Despite the prevalent use of alerting sounds in alarms and human-machine interface systems, we have only a rudimentary understanding of what determines auditory salience - the automatic attraction of attention by sound - and the brain mechanisms which underlie this process. A major roadblock to understanding has been the lack of a robust, objective means of quantifying sound-driven attentional capture. Here we demonstrate that microsaccade inhibition - an oculomotor response arising within 300ms of sound onset - is a robust marker for the salience of brief sounds. Specifically, we show that a crowd sourced (N=911) subjective salience ranking correlated robustly with the superior colliculus (SC) mediated ocular freezing response measured in naive, passively listening participants (replicated in two groups of N=15 each). More salient sounds evoked earlier microsaccadic inhibition, consistent with a faster orienting response. These results establish that microsaccade-indexed activity within the SC is a practical objective measure for auditory salience.\n\nSignificance statementMicrosaccades are small, rapid, fixational eye movements, measurable with sensitive eye-tracking equipment. We reveal a novel, robust link between microsaccade dynamics and the subjective salience of brief sounds (salience ranking obtained from a large number of participants in an online experiment): Within 300 ms of sound onset, the eyes of naive, passively listening participants demonstrate different microsaccade patterns as a function of the sounds perceptual salience. These results position the Superior Colliculus (the generator of microsaccades) as an important brain area to investigate in the context of a putative multi-modal salience-hub and establish a robust objective means for quantifying auditory salience, critical in the development of sound-based human machine interfaces.

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↗

bioRxiv2017

Decoding the auditory brain with canonical component analysis

The relation between a stimulus and the brain response that it evokes can shed light on perceptual processes within the brain. It can also be harnessed to control external devices for Brain Computer Interface (BCI) applications. While the classic event-related potential (ERP) is appropriate for isolated stimuli, more sophisticated \"decoding\" strategies are needed to address ongoing stimuli such as speech, music or environmental sounds. Here we describe an approach based on Canonical Correlation Analysis (CCA) that finds the optimal transform to apply to both stimulus and response to reveal correlations between the two. Compared to prior methods based on forward or backward models for stimulus-response mapping, CCA finds significantly higher correlation scores, thus providing increased sensitivity to relatively small effects, and supports classifier schemes that yield higher classification scores. CCA strips the brain response of variance unrelated to the stimulus, and the stimulus representation of variance that does not affect the response, and thus improves our observation of the relation between stimulus and response.

neuroscience↗