A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in ful llment of the requirements for the degree of Master of Science. October 31, 2016. / Brain Computer Interface (BCI) technology is a promising new channel for communication between humans and computers, and consequently other humans. This technology has the potential to form the basis for a paradigm shift in communication for people with disabilities or neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended to allow a user of possibly low income means to issue control signals to a computer by using modulated cortical activation patterns as a control signal. The user achieves this modulation by performing a mental task such as imagining waving the left arm until the computer performs the action intended by the user. In our work, we make use of the Emotiv EPOC headset to perform the EEG measurements. We validate our models by assessing their performance when the experimental data is collected using clinical-grade EEG technology. We make use of a publicly available data-set in the validation phase. We apply signal processing concepts to extract the power spectrum of each electrode from the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT). Specific bands in the power spectra are used to construct a vector that represents an abstract state the brain is in at that particular moment. The selected bands are motivated by insights from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract classification result which can then used to select the appropriate set of instructions to be executed by the computer. In our work, we make use of probabilistic graphical models to perform this association. The performance of two probabilistic graphical models is evaluated in this work. As a preliminary step, we perform classification on pre-segmented data and we assess the performance of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial structure such that each data le contains the power spectra measurements associated with only one mental task. The objective of the assessment is to determine how well the HCRF models the spatio-spectral and temporal relationships in the EEG data when mental tasks are performed in the aforementioned manner. In other words, the HCRF is to model the internal dynamics of the data corresponding to the mental task. The performance of the HCRF is assessed over three and four classes. We find that the HCRF can model the internal structure of the data corresponding to different mental tasks. As the final step, we perform classification on continuous data that is not segmented and assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF is used to perform sequence segmentation and labeling at each time-step so as to allow the program to determine which action should be taken at that moment. The sequence segmentation and labeling is the primary capability that we require in order to facilitate an asynchronous BCI protocol. The continuous data has a trial structure such that each data le contains the power spectra measurements associated with three different mental tasks. The mental tasks are randomly selected at 15 second intervals. The objective of the assessment is to determine how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data, both within each mental task and in the transitions between mental tasks. The performance of the LDCRF is assessed over three classes for both the publicly available data and the data we obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive classification rate of 82.31% averaged over three subjects, on the validation data which is in the publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF produces a true positive classification rate of 42.55% averaged over two subjects. In the two assessments involving the LDCRF, the random classification strategy would produce a true positive classification rate of 33.34%. It is thus clear that our classification strategy provides above random performance on the two groups of data-sets. We conclude that our results indicate that creating low-cost EEG based BCI technology holds potential for future development. However, as discussed in the final chapter, further work on both the software and low-cost hardware aspects is required in order to improve the performance of the technology as it relates to the low-cost context. / LG2017

Thesis (Ph. D.)--University of Washington, 2008. / Vita. Includes bibliographical references (leaves 102-123).

Thesis (Ph. D.) -- California Institute of Technology, 2008. / Title from home page (viewed 06/25/2010). Advisor and committee chair names found in the thesis' metadata record in the digital repository. Includes bibliographical references.

Thesis (Ph. D.)--Duke University, 2007.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 89-102). Also available in print.

Electroencephalogram (EEG) Recording has been through a lot of changes and modification since it was first introduced in 1929 due to rising technologies and signal processing advancements. The EEG Data acquisition stage is the first and most valuable component in any EEG recording System, it has the role of gathering and conditioning its input and outputting reliable data to be effectively analyzed and studied by digital signal processors using sophisticated and advanced algorithms which help in numerous medical and consumer applications. We have designed a low noise low power EEG data acquisition system that can be set to act as a standalone mobile EEG data processing unit providing data preprocessing functions; it can also be a very reliable high speed data acquisition interface to an EEG processing unit. / by Sherif S. Fathalla. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

This thesis discusses three projects that revolve around the central concept of the control of artificial hands. The first part of the thesis discusses the design of a museum exhibit for the South Florida Science Center that allows the public to control an i-limb Revolution prosthetic hand using electromyograph (EMG) sensors. A custom armature was designed to house the EMG sensors that are used to control the prosthesis. The top arm of the armature utilized a double rocker design for a greater range of motion which allows the display to accommodate arm sizes ranging from small children to large adults. This display became open to the public in March of 2019. The second part of the thesis describes a new concept for a simultaneous multi-object grasp using the Shadow hand robotic hand. This grasp is tested in an experiment that involves grasp and transportation tasks. This experiment also aims to analyze the benefit of soft robotic haptic feedback armband during the grasp and transportation tasks when a simulated break threshold is imposed on the objects. The usefulness of the haptic feedback was further tested with a guess the object task where the subjects had to determine which object was in the hand based solely off the armband. The new grasp synergy was deemed a success as all subjects were able to use the control method effectively with very little initial training. It was also found that the haptic feedback greatly aided in the successfully completing the transportation tasks. The human subjects were asked to rate the haptic feedback after each task, the overall rating for the helpfulness of the haptic feedback was rated as 4.6 out of 5. The final part of the thesis discusses an approach at gaining additional control signals for a dexterous artificial hand using a brain computer interface. This project seeks to investigate three neuromarkers for control which are: mu, xi and alpha. During analysis, the mu rhythm was not seen in our subject but alpha and xi were. Using deep learning approaches at classification, we were able to classify alpha and xi with at least a 90 percent accuracy. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010. / Committee Member: Alexander Gray; Committee Member: Charles Lee Isbell Jr.; Committee Member: Melody Moore Jackson; Committee Member: Paul M. Corballis; Committee Member: Thad Starner. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 76-84).