The rapid advancements in the field of machine learning and artificial intelligence has led to the emergence of technologies like the Brain Computer Interface (BCI), which has revolutionized rehabilitation protocols. However, given the neural basis of BCIs and the dependence of its performance on cognitive factors, BCIs may be used to characterize the functional capacity of the user. A resting state segment can also be considered for characterization of the functional network integrity, creating a two part framework that probes the functional networks and their cognitive manifestations. This thesis explores such a two part framework using a simultaneous EEG-fMRI setup on a healthy population. The BCI accuracies for all subjects increased over the course of the scan and is thought to be due to learning processes on the subject's part. Since such learning processes require cognitive faculties such as attention and working memory, these factors might modulate the BCI performance profile, making it a potential metric for the integrity of such cognitive factors. The resting state analysis identified four EEG Microstates that have been previously found to be associated with verbal, visual, saliency and attention reorientation tasks. The proportion of each microstate that composed the corresponding fMRI resting state networks (RSN) were identified, opening up the potential for predicting fMRI-based RSN information, from EEG microstates alone. The developed protocol can be used to diagnose potential conditions that negatively affect the functional capacity of the user by using the results from this study as healthy control data. This is the first known BCI based system for characterization of the user's functional integrity, opening up the possibility of using BCIs as a metric for diagnosing a neuropathology. / Thesis / Master of Applied Science (MASc)

This work seeks to more fully characterize how the representation of English vowels changes with increasing sound level in the frequency following response (FFR) of normal-hearing adult subjects. It further seeks to help inform the design of brain-computer interfaces (BCI) that exploit the FFR for hearing aid (HA) applications. The results of three studies are presented, followed by a theoretical examination of the potential BCI space as it relates to HA design. The first study examines how the representation of a long vowel changes with level in normal hearing subjects. The second study examines how the representation of four short vowels changes with level in normal hearing subjects. The third study utilizes machine learning techniques to automatically classify the FFRs captured in the second study. Based in-part on the findings from these three studies, potential avenues to pursue with respect to the utilization of the FFR in the automated fitting of HAs are proposed. The results of the first two studies suggest that the FFR to vowel stimuli presented at levels in the typical speech range provide robust and differentiable representations of both envelope and temporal fine structure cues present in the stimuli in both the time and frequency domains. The envelope FFR at the fundamental frequency (F0) was generally not monotonic-increasing with level increases. The growth of the harmonics of F0 in the envelope FFR were consistent indicators of level-related effects, and the harmonics related to the first and second formants were also consistent indicators of level effects. The third study indicates that common machine-learning classification algorithms are able to exploit features extracted from the FFR, both in the time and frequency domains, in order to accurately predict both vowel and level classes among responses. This has positive implications for future work regarding BCI-based approaches to HA fitting, where controlling for clarity and loudness are important considerations.

abstract: Optical Fibers coupled to laser light sources, and Light Emitting Diodes are the two classes of technologies used for optogenetic experiments. Arizona State University's Flexible Display Center fabricates novel flexible Organic Light Emitting Diodes(OLEDs). These OLEDs have the capability of being monolithically fabricated over flexible, transparent plastic substrates and having power efficient ways of addressing high density arrays of LEDs. This thesis critically evaluates the technology by identifying the key advantages, current limitations and experimentally assessing the technology in in-vivo and in-vitro animal models. For in-vivo testing, the emitted light from a flat OLED panel was directly used to stimulate the neo-cortex in the M1 region of transgenic mice expressing ChR2 (B6.Cg-Tg (Thy1-ChR2/EYFP) 9Gfng/J). An alternative stimulation paradigm using a collimating optical system coupled with an optical fiber was used for stimulating neurons in layer 5 of the motor cortex in the same transgenic mice. EMG activity was recorded from the contralateral vastus lateralis muscles. In vitro testing of the OLEDs was done in primary cortical neurons in culture transfected with blue light sensitive ChR2. The neurons were cultured on a microelectrode array for taking neuronal recordings. / Dissertation/Thesis / ICMS response in front and hind limb / Optogenetic response using iLEDs and OLEDs / iLED vs iLED coupled to optical fiber response / Masters Thesis Bioengineering 2015

Brain-computer interfacing (BCI) is a new method of human-machine interaction. It involves the extraction of information from the electroencephalogram (EEG) through signal processing and pattern recognition. The technology has far reaching implications for those with severe physical disabilities and has the potential to enhance machine interaction for the rest of the population. In this work we investigate time-frequency analysis in motor-imagery BCI. We consider two methods for signal analysis: adaptive autoregressive models (AAR) and wavelet transform (WAV). There are three major contributions of this research to single-trial analysis in motor-imagery BCI. First, we improve classification of AAR features over a conventional method by applying a temporal evidence accumulation (TEA) framework. Second, we compare the performance of AAR and WAV under the TEA framework for three subjects and find that WAV outperforms AAR for two subjects. The subject for whom AAR outperforms WAV has the lowest overall signal-to-noise ratio in their BCI output, an indication that the AAR model is more robust than WAV for noisier signals. Lastly, we find empirical evidence of complimentary information between AAR and WAV and propose a fusion scheme that increases the mutual information between the BCI output and classes.

Brain-computer interfacing (BCI) is a new method of human-machine interaction. It involves the extraction of information from the electroencephalogram (EEG) through signal processing and pattern recognition. The technology has far reaching implications for those with severe physical disabilities and has the potential to enhance machine interaction for the rest of the population. In this work we investigate time-frequency analysis in motor-imagery BCI. We consider two methods for signal analysis: adaptive autoregressive models (AAR) and wavelet transform (WAV). There are three major contributions of this research to single-trial analysis in motor-imagery BCI. First, we improve classification of AAR features over a conventional method by applying a temporal evidence accumulation (TEA) framework. Second, we compare the performance of AAR and WAV under the TEA framework for three subjects and find that WAV outperforms AAR for two subjects. The subject for whom AAR outperforms WAV has the lowest overall signal-to-noise ratio in their BCI output, an indication that the AAR model is more robust than WAV for noisier signals. Lastly, we find empirical evidence of complimentary information between AAR and WAV and propose a fusion scheme that increases the mutual information between the BCI output and classes.

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