EEG artifact removal at the edge using AI hardware

Research output: Contribution to conferencePosterpeer-review

Abstract

Wearable EEG enables non-invasive recording of brain signals when individuals engage in day-to-day activities. They are widely used in conditions such as epilepsy. RELIEVE https://project-relieve.eu/project-profile/ is an EU-funded project creating a wearable EEG unit, machine-learning models, and an ultrasound vagus nerve stimulator, which could ultimately be employed for applications in epilepsy management. Although many AI models exist for EEG artifact removal, to date they have not been deployed on edge hardware for real-time processing. In the EEG sensor device itself. Here, we report our progress in implementing a deep autoencoder network for artifact removal on edge hardware.
Original languageEnglish
Publication statusAccepted/In press - 26 Sept 2024
EventIEEE Sensors 2024 - Kobe Portopia Hotel, Kobe, Japan
Duration: 20 Oct 202423 Oct 2024
https://2024.ieee-sensorsconference.org/

Conference

ConferenceIEEE Sensors 2024
Country/TerritoryJapan
CityKobe
Period20/10/2423/10/24
Internet address

Keywords

  • Electroencephalography
  • Artifacts
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Edge Computing

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