Search Results for author:
Found 14 papers, 4 papers with code
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses
In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus.
Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework
We modeled electroencephalography (EEG) responses to acoustic, segmentation, and linguistic speech representations of a story using convolutional neural networks trained on a large sample of healthy participants, serving as a model for intact neural tracking of speech.
Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data
Linear Discriminant Analysis (LDA) is one of the oldest and most popular linear methods for supervised classification problems.
The role of vowel and consonant onsets in neural tracking of natural speech
Our results show that vowel-consonant onsets outperform onsets of any phone in both tasks, which suggests that neural tracking of the vowel vs. consonant exists in the EEG to some degree.
Detecting post-stroke aphasia using EEG-based neural envelope tracking of natural speech
In this study, we aimed to test the potential of the neural envelope tracking technique for detecting language impairments in individuals with aphasia (IWA).
Relating EEG to continuous speech using deep neural networks: a review
Linear models are presently used to relate the EEG recording to the corresponding speech signal.
Self-supervised enhancement of stimulus-evoked brain response data
We propose a novel paradigm for the self-supervised enhancement of stimulus-related brain response data.
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
We show the superiority of the proposed stimulus-informed GCCA method based on the inter-subject correlation between electroencephalography responses of a group of subjects listening to the same speech stimulus, especially for lower amounts of data or smaller groups of subjects.
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG).
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech.
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model
Decoding the speech signal that a person is listening to from the human brain via electroencephalography (EEG) can help us understand how our auditory system works.
Predicting speech intelligibility from EEG in a non-linear classification paradigm
The accuracy of the model's match/mismatch predictions can be used as a proxy for speech intelligibility without subject-specific (re)training.
EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices
People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously.
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses
OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy, two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest.
