Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI

Noeline W. Prins, Shijia Geng, Eric A. Pohlmeyer, Babak Mahmoudi, Justin C. Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.

Original languageEnglish (US)
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Pages5250-5253
Number of pages4
DOIs
StatePublished - Oct 31 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: Jul 3 2013Jul 7 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period7/3/137/7/13

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Prins, N. W., Geng, S., Pohlmeyer, E. A., Mahmoudi, B., & Sanchez, J. C. (2013). Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (pp. 5250-5253). [6610733] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2013.6610733