During the past decade, several studies have identified electroencephalographic (EEG) correlates of selective auditory attention to speech. In these studies, typically, listeners are instructed to focus on one of two concurrent speech streams (the "target"), while ignoring the other (the "masker"). EEG signals are recorded while participants are performing this task, and subsequently analyzed to recover the attended stream. An assumption often made in these studies is that the participant's attention can remain focused on the target throughout the test. To check this assumption, and assess when a participant's attention in a concurrent speech listening task was directed toward the target, the masker, or neither, we designed a behavioral listen-then-recall task (the Long-SWoRD test). After listening to two simultaneous short stories, participants had to identify keywords from the target story, randomly interspersed among words from the masker story and words from neither story, on a computer screen. To modulate task difficulty, and hence, the likelihood of attentional switches, masker stories were originally uttered by the same talker as the target stories. The masker voice parameters were then manipulated to parametrically control the similarity of the two streams, from clearly dissimilar to almost identical. While participants listened to the stories, EEG signals were measured and subsequently, analyzed using a temporal response function (TRF) model to reconstruct the speech stimuli. Responses in the behavioral recall task were used to infer, retrospectively, when attention was directed toward the target, the masker, or neither. During the model-training phase, the results of these behavioral-data-driven inferences were used as inputs to the model in addition to the EEG signals, to determine if this additional information would improve stimulus reconstruction accuracy, relative to performance of models trained under the assumption that the listener's attention was unwaveringly focused on the target. Results from 21 participants show that information regarding the actual - as opposed to, assumed - attentional focus can be used advantageously during model training, to enhance subsequent (test phase) accuracy of auditory stimulus-reconstruction based on EEG signals. This is the case, especially, in challenging listening situations, where the participants' attention is less likely to remain focused entirely on the target talker. In situations where the two competing voices are clearly distinct and easily separated perceptually, the assumption that listeners are able to stay focused on the target is reasonable. The behavioral recall protocol introduced here provides experimenters with a means to behaviorally track fluctuations in auditory selective attention, including, in combined behavioral/neurophysiological studies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8710602 | PMC |
http://dx.doi.org/10.3389/fnins.2021.674112 | DOI Listing |
Food Chem
January 2025
Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai 200240, China; School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China. Electronic address:
This study investigates the flavor perception of strong-aroma Baijiu through physiological electrical signals, focusing on electroencephalography (EEG) and electromyography (EMG) during olfactory and gustatory evaluations. It examines how sensory qualities, especially mellowness, influence brain and muscle responses. Results showed significant differences in EEG δ and β wavebands, mainly in the frontal and temporal lobes, reflecting varying brain activities across Baijiu types.
View Article and Find Full Text PDFEpilepsy Behav
January 2025
Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address:
Purpose: Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to assist in the presurgical localization of seizure foci in people with epilepsy. Our study aimed to examine the clinical feasibility of an optimized concurrent EEG-fMRI protocol.
Methods: The optimized protocol employed a fast-fMRI sequence (sampling rate = 10 Hz) with a spare arrangement, which allowed a time window of 1.
Hear Res
January 2025
Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom.
The cortical tracking of the acoustic envelope is a phenomenon where the brain's electrical activity, as recorded by electroencephalography (EEG) signals, fluctuates in accordance with changes in stimulus intensity (the acoustic envelope of the stimulus). Understanding speech in a noisy background is a key challenge for people with hearing impairments. Speech stimuli are therefore more ecologically valid than clicks, tone pips, or speech tokens (e.
View Article and Find Full Text PDFPLoS One
January 2025
Dept. of Psychiatry, Harvard Medical School, McLean Hospital, Belmont, Massachusetts, United States of America.
Opioid dependence is defined by an aversive withdrawal syndrome upon drug cessation that can motivate continued drug-taking, development of opioid use disorder, and precipitate relapse. An understudied but common opioid withdrawal symptom is disrupted sleep, reported as both insomnia and daytime sleepiness. Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions.
View Article and Find Full Text PDFSci Adv
January 2025
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!