Target speaker identification is essential for speech enhancement algorithms in assistive devices aimed toward helping the hearing impaired. Several recent studies have reported that target speaker identification is possible through electroencephalography (EEG) recordings. If the EEG system could be reduced to acceptable size while retaining the signal quality, hearing aids could benefit from the integration with concealed EEG. To compare the performance of a multichannel around-the-ear EEG system with high-density cap EEG recordings an envelope tracking algorithm was applied in a competitive speaker paradigm. The data from 20 normal hearing listeners were concurrently collected from the traditional state-of-the-art laboratory wired EEG system and a wireless mobile EEG system with two bilaterally-placed around-the-ear electrode arrays (cEEGrids). The results show that the cEEGrid ear-EEG technology captured neural signals that allowed the identification of the attended speaker above chance-level, with 69.3% accuracy, while cap-EEG signals resulted in the accuracy of 84.8%. Further analyses investigated the influence of ear-EEG signal quality and revealed that the envelope tracking procedure was unaffected by variability in channel impedances. We conclude that the quality of concealed ear-EEG recordings as acquired with the cEEGrid array has potential to be used in the brain-computer interface steering of hearing aids.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4961688 | PMC |
http://dx.doi.org/10.3389/fnins.2016.00349 | DOI Listing |
Prog Biophys Mol Biol
December 2024
Molecular Biotechnology, Turkish-German University, Sahinkaya Caddesi No. 106, Beykoz, Istanbul 34820 Turkey. Electronic address:
The intersection of electromagnetic radiation and neuronal communication, focusing on the potential role of biophoton emission in brain function and neurodegenerative diseases is an emerging research area. Traditionally, it is believed that neurons encode and communicate information via electrochemical impulses, generating electromagnetic fields detectable by EEG and MEG. Recent discoveries indicate that neurons may also emit biophotons, suggesting an additional communication channel alongside the regular synaptic interactions.
View Article and Find Full Text PDFZh Nevrol Psikhiatr Im S S Korsakova
December 2024
Novosibirsk State Medical University, Novosibirsk, Russia.
Objective: To evaluate the effectiveness of complex rehabilitation measures using the drug Cortexin in children with neuropsychiatric pathology during a one-year follow-up.
Material And Methods: A promising dynamic examination and treatment of 323 children with neuropsychiatric pathology from the age of 7 days to 1 year, age 3.2±1.
Sleep
December 2024
Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Study Objectives: Isolated REM sleep behavior disorder (iRBD) is recognized as a prodromal stage of alpha-synucleinopathies. Predicting phenoconversion in iRBD patients remains a key challenge. We aimed to investigate whether event-related potentials (ERPs) recorded during visuospatial attention task can serve as predictors of phenoconversion in iRBD patients.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Neurosurgery, Baylor College of Medicine, Houston, USA.
Alteration of responses to salient stimuli occurs in a wide range of brain disorders and may be rooted in pathophysiological brain state dynamics. Specifically, tonic and phasic modes of activity in the reticular activating system (RAS) influence, and are influenced by, salient stimuli, respectively. The RAS influences the spectral characteristics of activity in the neocortex, shifting the balance between low- and high-frequency fluctuations.
View Article and Find Full Text PDFJ Biomed Phys Eng
December 2024
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.
Objective: The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).
Material And Methods: In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!