Classifying coherent versus nonsense speech perception from EEG using linguistic speech features.

When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants.

The role of vowel and consonant onsets in neural tracking of natural speech.

. To investigate how the auditory system processes natural speech, models have been created to relate the electroencephalography (EEG) signal of a person listening to speech to various representations of the speech. Mainly the speech envelope has been used, but also phonetic representations.

Robust neural tracking of linguistic speech representations using a convolutional neural network.

When listening to continuous speech, populations of neurons in the brain track different features of the signal. Neural tracking can be measured by relating the electroencephalography (EEG) and the speech signal. Recent studies have shown a significant contribution of linguistic features over acoustic neural tracking using linear models.

Relating EEG to continuous speech using deep neural networks: a review.

When a person listens to continuous speech, a corresponding response is elicited in the brain and can be recorded using electroencephalography (EEG). Linear models are presently used to relate the EEG recording to the corresponding speech signal. The ability of linear models to find a mapping between these two signals is used as a measure of neural tracking of speech.

Decoding of the speech envelope from EEG using the VLAAI deep neural network.

To investigate the processing of speech in the brain, commonly simple linear models are used to establish a relationship between brain signals and speech features. However, these linear models are ill-equipped to model a highly-dynamic, complex non-linear system like the brain, and they often require a substantial amount of subject-specific training data. This work introduces a novel speech decoder architecture: the Very Large Augmented Auditory Inference (VLAAI) network.

Predicting speech intelligibility from EEG in a non-linear classification paradigm.

Currently, only behavioral speech understanding tests are available, which require active participation of the person being tested. As this is infeasible for certain populations, an objective measure of speech intelligibility is required. Recently, brain imaging data has been used to establish a relationship between stimulus and brain response.

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding.

Objective: A hearing aid's noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on electroencephalography recordings.

Monitoring activities of daily living using Wireless Acoustic Sensor Networks in clean and noisy conditions.

This work examines the use of a Wireless Acoustic Sensor Network (WASN) for the classification of clinically relevant activities of daily living (ADL) of elderly people. The aim of this research is to automatically compile a summary report about the performed ADLs which can be easily interpreted by caregivers. In this work, the classification performance of the WASN will be evaluated in both clean and noisy conditions.