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Filtering by: Creator Chestek, Cynthia A Discipline Engineering

Dataset for "Error detection and correction in intracortical brain–machine interfaces controlling two finger groups"

Creator:

Wallace, Dylan M, Benyamini, Miri, Nason-Tomaszewski, Samuel R, Costello, Joseph T, Cubillos, Luis H, Mender, Matthew J, Temmar, Hisham, Willsey, Matthew S, Patil, Parag P, Chestek, Cynthia A, and Zacksenhouse, Miriam

Description:

This is data from Wallace, Benyamini et al., 2023, Journal of Neural Engineering. There are two sets of data included: 1. Neural features and error labels used to train error classifiers for each day used in the study 2. Trial data from an example experiment day (Monkey N, Day 6), with runs for offline calibration, online brain control, error monitoring, and error correction. The purpose of this study was to investigate the use of error signals in motor cortex to improve brain-machine interface (BMI) performance for control of two finger groups. All data is contained in .mat files, which can be opened using MATLAB or the Python SciPy library.

Keyword:

Brain-machine interface (BMI), Error detection, and Neural recording

Citation to related publication:

Wallace, D. M., Benyamini, M., Nason-Tomaszewski, S. R., Costello, J. T., Cubillos, L. H., Mender, M. J., Temmar, H., Willsey, M. S., Patil, P. G., Chestek, C. A., & Zacksenhouse, M. (2023). Error detection and correction in intracortical brain–machine interfaces controlling two finger groups. Journal of Neural Engineering, 20(4), 046037. https://doi.org/10.1088/1741-2552/acef95

Discipline:

Engineering, Science, and Health Sciences
Electromyography from bipolar electrodes implanted into Regenerative Peripheral Nerve Interfaces and residual muscles in persons with amputations

Creator:

Vaskov, Alex K, Vu, Philip P, North, Naia, Davis, Alicia J, Kung, Theodore A, Gates, Deanna H, Cederna, Paul S, and Chestek, Cynthia A

Description:

The data was used to calibrate and simulate pattern recognition algorithms for the following publication: Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands (medRxiv 2020, IEEE TRO in review). Each data file is named as follows Px_PostureSet.csv. Where Px is the patient number. The 1 of 10 posture set contains individual finger and intrinsic thumb movements, the grasps posture set contains a fewer number of combined finger movements. P1’s calibration data for individual fingers is labelled 1 of 12 because it also includes two grasps, which were removed for analysis in the publication. The first column of each .csv file is the experiment time in seconds. The second column is the posture of the cue hand at that timestamp. The rest of the columns are the raw EMG data in microvolts sampled at 30KSps. A legend of the movement postures, each patients EMG channels, and suggested signal processing and filtering is included in DataLabellingAndProcessing.pdf

Keyword:

pattern recognition, electromyography, regenerative peripheral nerve interface, intramuscular electrodes, and myoelectric prostheses

Citation to related publication:

Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands A. K. Vaskov, P. P. Vu, N. North, A. J. Davis, T. A. Kung, D. H. Gates, P. S. Cederna, C. A. Chestek medRxiv 2020.10.28.20217273; doi: https://doi.org/10.1101/2020.10.28.20217273

Discipline:

Science and Engineering

Dataset for "Error detection and correction in intracortical brain–machine interfaces controlling two finger groups"

Electromyography from bipolar electrodes implanted into Regenerative Peripheral Nerve Interfaces and residual muscles in persons with amputations

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Dataset for "Error detection and correction in intracortical brain–machine interfaces controlling two finger groups"

Electromyography from bipolar electrodes implanted into Regenerative Peripheral Nerve Interfaces and residual muscles in persons with amputations

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