Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
Authors/Creators
- 1. Northwestern University
- 2. Meta Reality Labs*
- 3. Université Laval
- 4. University of Montreal
Description
Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the "decoder" at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called "Adversarial Domain Adaptation Network" (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.
Notes
This data set includes behavioral recordings and extracellular neural recordings from the primary motor cortex of six rhesus macaque. Christian Ethier, Stephanie N. Thacker, Matthew G. Perich and Xuan Ma collected these data in the laboratory of Lee E. Miller in Northwestern University. Xuan Ma and Fabio Rizzoglio processed the data for use in [1], which utilized adversarial neural networks to "align" the neural signals over a long period of time to extend the accuracy of an iBCI decoder calibrated only on the very first day (day-0).
Please cite this article if you are using this dataset:
[1] Ma X, Rizzoglio F, Perreault EJ, Miller LE, Kennedy A. Using adversarial networks to extend brain computer interface decoding accuracy over time. eLife 2023;12:e84296. DOI: https://doi.org/10.7554/eLife.84296
This dataset contains data files from 6 monkeys and 4 types of behavioral tasks (see Methods). The data files from one monkey/task were compressed into a `.7z` file, which could be opened and extracted by '7-Zip'. The `.7z` files are named like 'Name_Task_XXXX.7z', where 'Name' is the full name of the monkey ('Jango': monkey J; 'Spike': monkey S; 'Greyson': monkey G; 'Pop': monkey P; 'Chewie': monkey C; 'Mihili': monkey M), 'Task' indicates the behavioral task ('ISO': isometric wrist task; 'PG': power grasp task; 'Key': key grasp task; 'CO': center-out reach task; 'RT': random-target reach task), and 'XXXX' indicates the year when the data files were recorded.
Data files could be extraxted from the `.7z` files once they are downloaded. Please check the readme.md file along with this data set and also this document to see more details about them.
Funding provided by: National Institutes of Health
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002
Award Number: R01 NS053603
Funding provided by: National Institutes of Health
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000002
Award Number: R01 NS074044
Files
ADAN_aligner.ipynb
Additional details
Related works
- Is cited by
- 10.1101/2022.08.26.504777 (DOI)
- 10.7554/elife.84296 (DOI)
- Is source of
- 10.5061/dryad.cvdncjt7n (DOI)