Biology subjects

Fuglsang, S. A.

Publications and source records attributed to Fuglsang, S. A..

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↗

bioRxiv2023

Exploring an EM-algorithm for banded regression in computational neuroscience

Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in decoding models. When fitting such models, it can be relevant to allow differential shrinkage of the different groups of regression weights. Here, we explore a framework that allow for straightforward definition and estimation of such models. We present an expectation-maximization algorithm for tuning hyperparameters that control shrinkage of groups of weights. We highlight properties, limitations, and potential use-cases of the model using simulated data. Next, we explore the model in the context of a BOLD fMRI encoding analysis and an EEG decoding analysis. Finally, we discuss cases where the model can be useful and scenarios where regularization procedures complicate model interpretation.

neuroscience↗

bioRxiv2022

Corticospinal excitability is influenced by the recent history of electrical digital stimulation: implications for the relative magnitude of short-latency afferent inhibition

BackgroundElectrical stimulation of the hand can suppress the motor evoked potential (MEP) elicited by transcranial magnetic stimulation (TMS) of the primary motor hand area (M1-HAND) when the afferent stimulus arrives in M1-HAND at the time of TMS. The magnitude of short-latency afferent inhibition (SAI) is expressed as the ratio between the conditioned and unconditioned MEP and is widely used to probe sensorimotor interactions in human physiological studies. Objective/HypothesisWe hypothesized that corticospinal excitability and SAI are influenced by the recent history of peripheral electrical stimulation. MethodsIn twenty healthy participants, we recorded MEPs from the right first dorsal interosseus muscle. MEPs were evoked by single-pulse TMS of the left M1-HAND alone (unconditioned TMS) or by TMS preceded by electrical stimulation of the right index finger ("homotopic" conditioning) or little finger ("heterotopic" conditioning). The three conditions were pseudo-randomly intermixed or delivered in blocks in which a single condition was repeated five or ten times. MEP amplitudes and SAI magnitudes were compared using linear mixed effect models. ResultsAll stimulation protocols consistently produced SAI, which was stronger after homotopic stimulation. Randomly intermingling the three stimulation conditions reduced the relative magnitude of homotopic and heterotopic SAI as opposed to blocked stimulation. The apparent attenuation of SAI was caused by a suppression of the unconditioned but not the conditioned MEP amplitude during the randomly intermixed condition. Conclusion(s)The recent history of afferent stimulation modulates corticospinal excitability. This "history effect" impacts on the relative magnitude of SAI depending on how conditioned and unconditioned responses are intermixed and needs to be taken into consideration when probing afferent inhibition and corticospinal excitability.

neuroscience↗