TY - CONF A1 - Windbühler, Anna A1 - Okkesim, Sükrü A1 - Christ, Olaf A1 - Mottaghi, Soheil A1 - Rastogi, Shavika A1 - Schmuker, Michael A1 - Baumann, Timo A1 - Hofmann, Ulrich G. T1 - Machine Learning Approaches to Classify Anatomical Regions in Rodent Brain from High Density Recordings T2 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022): 11-15 July 2022, Glasgow, Scotland, United Kingdom N2 - Identifying different functional regions during a brain surgery is a challenging task usually performed by highly specialized neurophysiologists. Progress in this field may be used to improve in situ brain navigation and will serve as an important building block to minimize the number of animals in preclinical brain research required by properly positioning implants intraoperatively. The study at hand aims to correlate recorded extracellular signals with the volume of origin by deep learning methods. Our work establishes connections between the position in the brain and recorded high-density neural signals. This was achieved by evaluating the performance of BLSTM, BGRU, QRNN and CNN neural network architectures on multisite electrophysiological data sets. All networks were able to successfully distinguish cortical and thalamic brain regions according to their respective neural signals. The BGRU provides the best results with an accuracy of 88.6 % and demonstrates that this classification task might be solved in higher detail while minimizing complex preprocessing steps. Y1 - 2022 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/3529 UR - https://nbn-resolving.org/urn:nbn:de:bvb:898-opus4-35296 SP - 3530 EP - 3533 PB - IEEE ER -