Skip to content
iLab: From Brain to Machine and Back

iLab: From Brain to Machine and Back

iossifidis Lab

  • Home
  • Research Projects
  • Publications
  • Software
  • Press and Media
  • Student’s Zone — Teaching
    • Thesis topics
    • Student Projects
    • Journal Club & Progress Club
    • Lab Course
    • Lectures – Vorlesungen
  • About us
    • The Team
    • Contact
    • Impressum
  • Home
  • 2021
  • August
  • 12
  • JournalClub: Adversarial Domain Adaptation For Stable Brain-Machine Interfaces

JournalClub: Adversarial Domain Adaptation For Stable Brain-Machine Interfaces

Posted on August 12, 2021August 12, 2021 By stephan No Comments on JournalClub: Adversarial Domain Adaptation For Stable Brain-Machine Interfaces
journal club

Farshchian, A., Gallego, J. A., Cohen, J. P., Bengio, Y., Miller, L. E., & Solla, S. A. (2018). Adversarial domain adaptation for stable brain-machine interfaces. arXiv preprint arXiv:1810.00045.

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.

Presented on 11.08.2021 by Stephan J. Lehmler

URL: Adversarial Domain Adaptation for Stable Brain-Machine Interfaces | DeepAI

Post navigation

❮ Previous Post: JournalClub: Cross-Domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG
Next Post: Towards wearable BCI systems that leverage contextual neuromechanics and edge-computing ❯

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recent Posts

  • The concepts of muscle activity generation driven by upper limb kinematics June 26, 2023
  • Vom Rollator für Kinder zum intelligenten Bewegungs–From rollator for children to intelligent movement coach – BmBF.START-Interaktiv December 27, 2022
  • Game control using an EMG and EEG-based BCI October 15, 2022
  • Bacherlorarbeit: Artificial Muscle Signal Generation using Generative Adversarial Networks October 15, 2022
  • Iossifidis Lab at the Bernstein Conference 2022 September 29, 2022

Recent Comments

    Copyright © Ioannis Iossifidis 2020 iLab: From Brain to Machine and Back.

    Theme: Oceanly by ScriptsTown

    We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
    Cookie SettingsAccept All
    Manage consent

    Privacy Overview

    This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
    Necessary
    Always Enabled
    Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
    CookieDurationDescription
    cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
    cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
    cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
    cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
    cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
    cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
    cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
    cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
    viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
    viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
    Functional
    Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
    Performance
    Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
    Analytics
    Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
    Advertisement
    Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
    Others
    Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
    SAVE & ACCEPT