Using topographical channel distribution to decode movement directions from Local Field Potentials

Vijay Aditya Tadipatri, Ahmed H. Tewfik, James Ashe, Giuseppe Pellizzer, Rahul Gupta

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

Abstract

In Brain Machine Interface (BMI), movement direction can be decoded using intra-cortical recordings such as Local Field Potentials (LFP). Due to the natural instability and non-stationarity of these recordings, it is difficult to develop decoders that remain consistent over time and are not affected by learning. This paper uses qualitative information based on the temporal and spatial distribution of inter-channel ranking. The image block processing technique is exploited, and the distribution of top ranked channels is calculated. We use this spatio-temporal distribution information to decode the movement direction via a maximum likelihood estimator. Our results indicate that the decoding power is consistent over a period of two weeks. On an average, we obtain an average classification accuracy of 51.9% versus 33.2% from traditional state-of-the-art technique over a two week period.

Original languageEnglish (US)
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages1835-1838
Number of pages4
DOIs
StatePublished - Nov 2 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
CountryUnited States
CityChicago, IL
Period3/30/114/2/11

Keywords

  • Brain Computer Interface
  • Local Field Potentials
  • Maximum Likelihood Estimator

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