@inproceedings{f2d3cc693f094b029bae6a9dba28259d,
title = "Decoding force from deep brain electrodes in Parkinsonian patients",
abstract = "Limitations of many Brain Machine Interface (BMI) systems using invasive electrodes include reliance on single neurons and decoding limited to kinematics only. This study investigates whether force-related information is present in the local field potential (LFP) recorded with deep brain electrodes using data from 14 patients with Parkinson's disease. A classifier based on logistic regression (LR) is developed to classify various force stages, using 10-fold cross validation. Least Absolute and Shrinkage Operator (Lasso) is then employed in order to identify the features with the most predictivity. The results show that force-related information is present in the LFP, and it is possible to distinguish between various force stages using certain frequency-domain (delta, beta, gamma) and time-domain (mobility) features in real-time.",
keywords = "Biomechanical Phenomena, Brain/physiopathology, Brain-Computer Interfaces, Electrodes, Humans, Neurons/physiology, Parkinson Disease/physiopathology",
author = "Shah, {Syed A} and {Huiling Tan} and Peter Brown",
year = "2016",
month = oct,
day = "18",
doi = "10.1109/EMBC.2016.7592025",
language = "English",
volume = "2016",
series = "Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5717--5720",
booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)",
}