I research the brain, how we can connect it to technology, and specifically how we can use technology to help understand speech production. My long-term goal is to establish innovative brain-computer interface paradigms for communication to improve the quality of life for individuals with communication disorders as well as the general population.

Decoding phonemes (speech sounds) from speech motor cortex

As part of my dissertation research, I developed a method for decoding phonemes and other aspects of speech production from electrocorticographic (ECoG) signals. Previously, ECoG had been used to generally characterize cortical activity during speech. My work was the first to identify distinct cortical signatures of individual phonemes during utterance of words. By developing algorithms and Matlab frameworks to precisely identify the onset of phonemes, I enabled an event-related analysis of the neural signals. I used decoding as a tool to identify distinct phoneme patterns, and I further employed machine learning analysis to determine how well the data fit theoretical models of speech production. In order to conduct this research, I established collaborations with neurosurgeons in two research hospitals and developed an experimental protocol that could be employed at both institutions. This work was important in establishing that decoding speech sounds from cortical signal is possible, and that ECoG could be used in development of a cortical prostheses for speech.

E. M. Mugler, J. L. Patton, R. D. Flint, Z. A. Wright, S. U. Schuele, J. Rosenow, J. J. Shih, D. J. Krusienski, M. W. Slutzky, “Direct classification of all American English phonemes using signals from functional speech motor cortex,” J. Neural Eng., Vol. 11, No. 3,  2014.

E. M. Mugler, R. D. Flint, Z. A. Wright, S. U. Schuele, J. L. Rosenow, J.L., Patton, M. W. Slutzky, “Decoding articulatory properties of overt speech from electrocorticography,” 5th International Brain Computer Interface Meeting, 2013.

E. M. Mugler, M. Goldrick, J. Rosenow, Matthew Tate, M. W. Slutzky, “Decoding of Articulatory Gestures During Word Production Using Speech Motor and Premotor Cortical Activity,” 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’15), Milan, Italy, August 25-29, 2015.

E. M. Mugler, M. Goldrick, M. W. Slutzky, ” Cortical encoding of phonemic context during word production,” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’14), Chicago, IL, August 26-30, 2014.

Creation of a neurally-controlled internet browser for people with ALS

During my Fulbright scholarship year, I trained and conducted research in Tübingen, Germany, with a team of researchers studying brain-computer interface (BCI) use in people with amyotrophic lateral sclerosis (ALS). This team had developed a BCI method for typing using event-related neural signals from electroencephalogaphy (EEG), but there was no method in place for using the BCI output for other applications. Users of the BCI system needed to access the internet, as patients wanted to look up information related to ALS and access online chat communities for people with ALS. For my project, I developed a software application that used the output of the BCI to control an internet browser. The software I developed executed navigational commands and enabled typical web surfing. I then worked with a research team to test and evaluate the BCI browser for accuracy, efficiency and accessbility in healthy patients and in people with ALS. Results were resoundingly positive, and people with ALS could use the browser for applications that I didn’t originally anticipate, such as purchasing books through Amazon. Patients, as well as healthy individuals, reported high satisfaction using our BCI Browser. Moreover, in publications, I established criteria for evaluating web browsers and other BCI applications, so that further advances in the field can be evaluated on a similar scale.

Mugler, E.M., Ruf, C.A., Halder, S., Bensch, M., and Kübler, A.  Design and Implementation of a P300-Based Brain-Computer Interface for Controlling an Internet Browser. IEEE Trans. on Neural Sys. & Rehab. Eng., Vol.18, No.6, pp.599-609, Dec. 2010. PMCID: 20805058

Mugler, E.M., Bensch, M., Halder, S., Rosenstiel, W., Bogdan, M., Birbaumer, N., and Kübler, A. Control of an Internet Browser Using the P300 Event-Related Potential. International Journal of Bioelectromagnetism. Vol. 10, No. 1, pp. 56 – 63, 2008.

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