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- All Subjects: EEG
- Creators: Daliri, Ayoub
- Member of: Theses and Dissertations
- Resource Type: Text
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
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.
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.