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.
authors.forename: Syed A
authors.initials: SA
authors.lastname: Shah
authors.lastname: Huiling Tan
authors.forename: Peter
authors.initials: P
authors.lastname: Brown
mesh_terms: D001696:Biomechanical Phenomena; D001921:Brain / Q000503:physiopathology*; D062207:Brain-Computer Interfaces; D004566:Electrodes; D006801:Humans; D009474:Neurons / Q000502:physiology; D010300:Parkinson Disease / Q000503:physiopathology*