This package contains Python code to run electrophysiology behavioral tasks, with emphasis on brain-machine interface (BMIs) tasks. This package differs from other similar packages (e.g., BCI2000--http://www.schalklab.org/research/bci2000) in that it is primarily intended for intracortical BMI experiments.

This package has been used with the following recording systems:

• Omniplex neural recording system (Plexon, Inc.).
• Blackrock NeuroPort

Code documentation can be found at http://carmenalab.github.io/bmi3d_docs/

Ramos Murguialday et al., A Novel Implantable Hybrid Brain-Machine-Interface (BMI) for Motor Rehabilitation in Stroke Patients. IEEE NER 2019 Khanna P. et al., Separating Motor Intention and Proprioceptive Neural Activity Patterns During Brain-Machine Interface (BMI) Control of a Wearable Multi-DOF Exoskeleton for Motor Rehabilitation in Severely Impaired Chronic Stroke Patients. IEEE NER 2019 Khanna P. and Carmena J.M. (2017) Beta band oscillations in motor cortex reflect neural population signals that delay movement onset. eLife 6:e24573. doi:10.7554/eLife.24573. Moorman H.G., Gowda S. and Carmena J.M. (2017) Control of redundant kinematic degrees of freedom in a closed-loop brain-machine interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(6), pp. 750-760. doi:10.1109/TNSRE.2016.2593696. Shanechi M.M., Orsborn A.L., Moorman H.G., Gowda S., Dangi S., and Carmena J.M. (2017) Rapid control and feedback rates in the sensorimotor pathway enhance neuroprosthetic control. Nature Communications 8:13825. doi:10.1038/ncomms13825. Dangi S., Gowda S., Moorman H.G., Orsborn A.L., So K., Shanechi M. and Carmena J.M. (2014) Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Computation 12, pp. 1-29.