Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
Authors: Ali Farshchian, Juan A. Gallego, Joseph P. Cohen, Yoshua Bengio, Lee E. Miller, Sara A. Solla
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The overall performance of the interface is summarized in Figure 2B, quantified using the percentage of the variance accounted for (%VAF) for five-fold cross-validated data. |
| Researcher Affiliation | Academia | Ali Farshchian, Juan A. Gallego, Lee E. Miller & Sara A. Solla Northwestern University, Evanston, IL, USA {a-farshchiansadegh,juan.gallego,lm,solla}@northwestern.edu Joseph P. Cohen & Yoshua Bengio University of Montreal, Montreal, Canada cohenjos@iro.umontreal.ca,yoshua.bengio@umontreal.ca |
| Pseudocode | No | The paper describes methods in text and equations but does not include any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology described, nor does it include any links to a code repository. |
| Open Datasets | No | The paper describes data collected from a male rhesus monkey for the experiment: 'To record neural activity, we implanted a 96-channel microelectrode array... Data was collected in five experimental sessions spanning 16 days.' There is no concrete access information (link, DOI, repository, or citation to an established public dataset) for this data. |
| Dataset Splits | Yes | The overall performance of the interface is summarized in Figure 2B, quantified using the percentage of the variance accounted for (%VAF) for five-fold cross-validated data. |
| Hardware Specification | No | The paper mentions 'a 96-channel microelectrode array (Blackrock Microsystems, Salt Lake City, Utah)' for neural activity recording, but it does not provide any specific hardware details such as GPU models, CPU types, or computational resources used for running the experiments or training the models. |
| Software Dependencies | No | The paper mentions various computational methods and models (e.g., LSTM, AE, CCA, GAN, VAE) but does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the implementation or experimentation. |
| Experiment Setup | Yes | The paper provides extensive details on the experimental setup, including the BMI architecture (neural autoencoder and EMG predictor), network layers and nonlinearities (five hidden units, linear readouts, exponential nonlinearity), data preprocessing (neural spikes binned at 50 ms, Gaussian filter with 125 ms standard deviation), and the composite loss function with an adaptive lambda parameter. |