AIMC Topic: Acoustic Stimulation
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Modality-independent representations of small quantities based on brain activation patterns.
Human brain mapping
Jan 9, 2016
Machine learning or MVPA (Multi Voxel Pattern Analysis) studies have shown that the neural representation of quantities of objects can be decoded from fMRI patterns, in cases where the quantities were visually displayed. Here we apply these technique...
Attentional Enhancement of Auditory Mismatch Responses: a DCM/MEG Study.
Cerebral cortex (New York, N.Y. : 1991)
Jan 16, 2015
Despite similar behavioral effects, attention and expectation influence evoked responses differently: Attention typically enhances event-related responses, whereas expectation reduces them. This dissociation has been reconciled under predictive codin...
CNNs improve decoding of selective attention to speech in cochlear implant users.
Journal of neural engineering
Jun 10, 2025
. Understanding speech in the presence of background noise such as other speech streams is a difficult problem for people with hearing impairment, and in particular for users of cochlear implants (CIs). To improve their listening experience, auditory...
Predicting artificial neural network representations to learn recognition model for music identification from brain recordings.
Scientific reports
May 29, 2025
Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict ...
Neural-WDRC: A Deep Learning Wide Dynamic Range Compression Method Combined With Controllable Noise Reduction for Hearing Aids.
Trends in hearing
Jan 1, 2025
Wide dynamic range compression (WDRC) and noise reduction both play important roles in hearing aids. WDRC provides level-dependent amplification so that the level of sound produced by the hearing aid falls between the hearing threshold and the highes...
Speech intelligibility prediction based on a physiological model of the human ear and a hierarchical spiking neural network.
The Journal of the Acoustical Society of America
Sep 1, 2024
A speech intelligibility (SI) prediction model is proposed that includes an auditory preprocessing component based on the physiological anatomy and activity of the human ear, a hierarchical spiking neural network, and a decision back-end processing b...
Performance and Reliability Evaluation of an Automated Bone-Conduction Audiometry Using Machine Learning.
Trends in hearing
Jan 1, 2024
To date, pure-tone audiometry remains the gold standard for clinical auditory testing. However, pure-tone audiometry is time-consuming and only provides a discrete estimate of hearing acuity. Here, we aim to address these two main drawbacks by develo...
ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.
Trends in hearing
Jan 1, 2024
Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods stru...
Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.
Brain : a journal of neurology
Feb 13, 2023
Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considera...
Hierarchical Learning of Statistical Regularities over Multiple Timescales of Sound Sequence Processing: A Dynamic Causal Modeling Study.
Journal of cognitive neuroscience
Jul 1, 2021
Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change ov...
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