# Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

arXiv: https://arxiv.org/abs/1810.00045 · HTML: https://mlsanity.com/paper/1810.00045/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 27 of 27)

- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Neighborhood Contrastive Learning Applied to Online Patient Monitoring](https://mlsanity.com/paper/2106.05142/) (2021)
- [Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond](https://mlsanity.com/paper/2204.08200/) (2022)
- [Gradual Domain Adaptation without Indexed Intermediate Domains](https://mlsanity.com/paper/2207.04587/) (2022)
- [Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity](https://mlsanity.com/paper/2111.02338/) (2021)
- [Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers](https://mlsanity.com/paper/2206.06131/) (2022)
- [Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation](https://mlsanity.com/paper/2202.06159/) (2022)
- [Generalizable, real-time neural decoding with hybrid state-space models](https://mlsanity.com/paper/2506.05320/) (2025)
- [SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding](https://mlsanity.com/paper/2507.08402/) (2025)
- [BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data](https://mlsanity.com/paper/2509.18627/) (2025)
- [Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models](https://mlsanity.com/paper/2306.06138/) (2023)
- [Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication](https://mlsanity.com/paper/2311.03611/) (2023)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)
- [Capturing cross-session neural population variability through self-supervised identification of consistent neuron ensembles](https://mlsanity.com/paper/2205.09829/) (2022)
- [Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptation](https://mlsanity.com/paper/2510.13864/) (2025)
- [Functional embeddings enable Aggregation of multi-area SEEG recordings over subjects and sessions](https://mlsanity.com/paper/2510.27090/) (2025)
- [Overcoming the Domain Gap in Neural Action Representations](https://mlsanity.com/paper/2112.01176/) (2021)
- [Selective Uncertainty Propagation in Offline RL](https://mlsanity.com/paper/2302.00284/) (2023)
- [SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation](https://mlsanity.com/paper/2501.19155/) (2025)
- [GDO:Gradual Domain Osmosis](https://mlsanity.com/paper/2501.19159/) (2025)
- [Nonlinear Dynamical Modeling of Human Intracranial Brain Activity with Flexible Inference](https://mlsanity.com/paper/2512.22785/) (2025)
- [ALIGN: Adversarial Learning for Generalizable Speech Neuroprosthesis](https://mlsanity.com/paper/2603.18299/) (2026)
- [Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface](https://mlsanity.com/paper/2607.24023/) (2026)
- [Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics](https://mlsanity.com/paper/2608.16569/) (2026)
- [Stable Neural Decoding Across Sessions via Task-Conditioned Latent Alignment for Brain-Machine Interfaces](https://mlsanity.com/paper/2609.27441/) (2026)
- [Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding](https://mlsanity.com/paper/2609.39080/) (2026)

## Abstract (arXiv)

Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These devices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of the brain. However, the inherent loss and turnover of recorded neurons requires repeated recalibrations of the interface, which can potentially alter the day-to-day user experience. The resulting need for continued user adaptation interferes with the natural, subconscious use of the BMI. Here, we introduce a new computational approach that decodes movement intent from a low-dimensional latent representation of the neural data. We implement various domain adaptation methods to stabilize the interface over significantly long times. This includes Canonical Correlation Analysis used to align the latent variables across days; this method requires prior point-to-point correspondence of the time series across domains. Alternatively, we match the empirical probability distributions of the latent variables across days through the minimization of their Kullback-Leibler divergence. These two methods provide a significant and comparable improvement in the performance of the interface. However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.