Filtering by
- All Subjects: EEG
- Creators: Harrington Bioengineering Program
- Creators: Daliri, Ayoub
- Member of: Theses and Dissertations
- Resource Type: Text
The cocktail party effect describes the brain’s natural ability to attend to a specific voice or audio source in a crowded room. Researchers have recently attempted to recreate this ability in hearing aid design using brain signals from invasive electrocorticography electrodes. The present study aims to find neural signatures of auditory attention to achieve this same goal with noninvasive electroencephalographic (EEG) methods. Five human participants participated in an auditory attention task. Participants listened to a series of four syllables followed by a fifth syllable (probe syllable). Participants were instructed to indicate whether or not the probe syllable was one of the four syllables played immediately before the probe syllable. Trials of this task were separated into conditions of playing the syllables in silence (Signal) and in background noise (Signal With Noise), and both behavioral and EEG data were recorded. EEG signals were analyzed with event-related potential and time-frequency analysis methods. The behavioral data indicated that participants performed better on the task during the “Signal” condition, which aligns with the challenges demonstrated in the cocktail party effect. The EEG analysis showed that the alpha band’s (9-13 Hz) inter-trial coherence could potentially indicate characteristics of the attended speech signal. These preliminary results suggest that EEG time-frequency analysis has the potential to reveal the neural signatures of auditory attention, which may allow for the design of a noninvasive, EEG-based hearing aid.
With millions of people living with a disease as restraining as migraines, there are no ways to diagnose them before they occur. In this study, a migraine model using nitroglycerin is used in rats to study the awake brain activity during the migraine state. In an attempt to search for a biomarker for the migraine state, we found multiple deviations in EEG brain activity across different bands. Firstly, there was a clear decrease in power in the delta, beta, alpha, and theta bands. A slight increase in power in the gamma and high frequency bands was also found, which is consistent with other pain-related studies12. Additionally, we searched for a decreased pain threshold in this deviation, in which we concluded that more data analysis is needed to eliminate the multiple potential noise influxes throughout each dataset. However, with this study we did find a clear change in brain activity, but a more detailed analysis will narrow down what this change could mean and how it impacts the migraine state.
stimuli played prior to the onset of speech production. In this experiment, we are examining the
specificity of the auditory stimulus by implementing congruent and incongruent speech sounds in
addition to non-speech sound. Electroencephalography (EEG) data was recorded for eleven adult
subjects in both speaking (speech planning) and silent reading (no speech planning) conditions.
Data analysis was accomplished manually as well as via generation of a MATLAB code to
combine data sets and calculate auditory modulation (suppression). Results of the P200
modulation showed that modulation was larger for incongruent stimuli than congruent stimuli.
However, this was not the case for the N100 modulation. The data for pure tone could not be
analyzed because the intensity of this stimulus was substantially lower than that of the speech
stimuli. Overall, the results indicated that the P200 component plays a significant role in
processing stimuli and determining the relevance of stimuli; this result is consistent with role of
P200 component in high-level analysis of speech and perceptual processing. This experiment is
ongoing, and we hope to obtain data from more subjects to support the current findings.
This thesis is a tutorial for a MATLAB user-interface, known as EEGLAB. Cognitive and neural correlates of analytical and insight processes were evaluated and analyzed in the CRAT using EEG. It was hypothesized that different EEG signals will be measured for analytical versus insight problem solving, primarily observed in the gamma wave production. The data was interpreted using EEGLAB, which allows psychological processes to be quantified based on physiological response. I have written a tutorial showing how to process the EEG signal through filtering, extracting epochs, artifact detection, independent component analysis, and the production of a time – frequency plot. This project has combined my interest in psychology with my knowledge of engineering and expand my knowledge of bioinstrumentation.