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- Creators: Computer Science and Engineering Program
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
Engaging users is essential for designers of any exhibit, such as the human-computer interface, the visual effects, or the informational content. The need to understand users’ experiences and learning gains has motivated a focus on user engagement across computer science. However, there has been limited review of how human-computer interaction research interprets and employs the concepts in museum and exhibit settings, specifically their joint effects. The purpose of this study is to assess users’ experience and learning outcome, while interacting with a web application part of an exhibit that showcases the NASA Psyche spacecraft model. This web application provides an interactive menu that allows the user to navigate on the touch panel installed within the Psyche Spacecraft Exhibit. The user can press the button on the menu which will light up the corresponding parts of the model with a detailed description displayed on the panel. For this study, participants were required to take a questionnaire, a pretest, and a posttest. They were also required to interact with the web application while wearing an Emotiv EPOC+ EEG headset that measures their emotions while they were visiting the exhibit. During the study, data such as questionnaire results, sensed emotions from the EEG headset, and pretest and posttest scores were collected. Using the information gathered, the study explores user experience and learning gains through both biometrics and traditional tools. The findings show that users felt engaged and frustrated the most and that users gained more knowledge but at varying degrees from the interaction. Future work can be done to lower the levels of frustration and keep learning gains at a more consistent rate by improving the exhibit design to better meet various learning needs and visitor profiles.