Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
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.
 This contribution describes a novel approach for implanted brain-machine interface in order to address calibration problem and covariate shift. A latent representation is extracted from SEEG signals and is the input of a LTSM trained to predict muscle activity. To mitigate the variation of neural activities across days, the authors compare a CCA approach, a Kullback-Leibler divergence minimization and a novel adversarial approach called ADAN. The authors evaluate their approach on 16-days recording of neurons from the motor cortex of rhesus monkey, along with EMG recording of corresponding the arm and hand. The results show that the domain adaptation from the first recording is best handled with the proposed adversarial scheme. Compared to CCA-based and KL-based approaches, the ADAN scheme is able to significantly improve the EMG prediction, requiring a relatively small calibration dataset. The individual variability in day-to-day brain signal is difficult to harness and this work offers an interesting approach to address this problem. The contributions are well described, the limitation of CCA and KL are convincing and are supported by the experimental results. The important work on the figure help to provide a good understanding of the benefit of this approach. Some parts could be improved. The results of Fig. 2B to investigate the role of latent variables extracted from the trained autoencoder are not clear, the simultaneous training could be better explained. As the authors claimed that their method allows to make an unsupervised alignment neural recording, independently of the task, an experiment on another dataset could enforce this claim.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This contribution describes a novel approach for implanted brain-machine interface in order to address calibration problem and covariate shift. A latent representation is extracted from SEEG signals and is the input of a LTSM trained to predict muscle activity. To mitigate the variation of neural activities across days, the authors compare a CCA approach, a Kullback-Leibler divergence minimization and a novel adversarial approach called ADAN. The authors evaluate their approach on 16-days recording of neurons from the motor cortex of rhesus monkey, along with EMG recording of corresponding the arm and hand. The results show that the domain adaptation from the first recording is best handled with the proposed adversarial scheme. Compared to CCA-based and KL-based approaches, the ADAN scheme is able to significantly improve the EMG prediction, requiring a relatively small calibration dataset. The individual variability in day-to-day brain signal is difficult to harness and this work offers an interesting approach to address this problem. The contributions are well described, the limitation of CCA and KL are convincing and are supported by the experimental results. The important work on the figure help to provide a good understanding of the benefit of this approach. Some parts could be improved. The results of Fig. 2B to investigate the role of latent variables extracted from the trained autoencoder are not clear, the simultaneous training could be better explained. As the authors claimed that their method allows to make an unsupervised alignment neural recording, independently of the task, an experiment on another dataset could enforce this claim.
assistant
 This contribution describes a novel approach for implanted brain-machine interface in order to address calibration problem and covariate shift. A latent representation is extracted from SEEG signals and is the input of a LTSM trained to predict muscle activity. To mitigate the variation of neural activities across days, the authors compare a CCA approach, a Kullback-Leibler divergence minimization and a novel adversarial approach called ADAN. The authors evaluate their approach on 16-days recording of neurons from the motor cortex of rhesus monkey, along with EMG recording of corresponding the arm and hand. The results show that the domain adaptation from the first recording is best handled with the proposed adversarial scheme. Compared to CCA-based and KL-based approaches, the ADAN scheme is able to significantly improve the EMG prediction, requiring a relatively small calibration dataset. The individual variability in day-to-day brain signal is difficult to harness and this work offers an interesting approach to address this problem. The contributions are well described, the limitation of CCA and KL are convincing and are supported by the experimental results. The important work on the figure help to provide a good understanding of the benefit of this approach. Some parts could be improved. The results of Fig. 2B to investigate the role of latent variables extracted from the trained autoencoder are not clear, the simultaneous training could be better explained. As the authors claimed that their method allows to make an unsupervised alignment neural recording, independently of the task, an experiment on another dataset could enforce this claim.