Master Thesis MSTR-2015-15

BibliographyKosch, Thomas: Real-Time Brain Mapping for Treating Substance Abuse using Neurofeedback.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 15 (2015).
83 pages, english.

Physiological sensors attached to a body become more and more integrated into the daily routine of everyone’s life. Since physiological sensors become cheaper and more compact, analyzing the state of the body is possible for everyone. Biofeedback describes a practice, that uses physiological sensors to expose the measurements in a visualized manner to the user. This feedback can be used to train mind and body to reach or avoid certain states. A subcategory of biofeedback is neurofeedback, which concerns with brain activity measurements. Brain activity is made available to a corresponding person, so that the person is able to train its brain towards a desired state. A lot of data is produced, which is hard to interpret manually in real-time. Finding patterns to detect neuropsychiatric diseases like Alzheimer, Parkinson or drug addiction requires an extensive analysis of the collected data. This thesis explains what bio- and neurofeedback is and provides an extensive analysis of related research, including a brief introduction about bioelectromagnetism and drug addiction. Furthermore, the effects of drug addiction on the brain will be explained. A big amount of data is generated by the brain activity measurement process. Suitable visualization modalities for interpretation are therefore required. This thesis also presents visualizations of the measured brain activity in real-time to make the data instantly available to a person interested in interpretation. Rather than just visualizing the electrical activity at certain spots, this thesis also proposes a visualization which makes the original electrical source visible which is responsible for the current measurements. This is useful when a person is interested for the reason of electrical activity at certain measurement spots. Neuropsychiatric diseases, especially drug addiction or Alzheimer, can be recognized and treated better. This thesis also explains the used and developed visualization algorithms. The technical realization is explained in detail which includes the implementation of a brain-computer interface, the utilization of an electrode placement system and the verification of the correctness of the implemented algorithms using neuromore Studio as base platform. On top of that, a neurofeedback session for treating drug addiction is performed to evaluate the usefulness of the developed visualizations. Eight participants took part in the study, which showed that the implemented algorithms can be used to interpret the impact of defined situations on the brain. Significant visual changes in brain activation could be found throughout most participants.

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Department(s)University of Stuttgart, Institute of Visualisation and Interactive Systems, Visualisation and Interactive Systems
Superviser(s)Schmidt, Prof. Albrecht; Hassib, Mariam A.; Mash, Dr. Deborah C.; Jillich, Benjamin
Entry dateJuly 30, 2018
   Publ. Computer Science