Learning context invariant representations for EEG data
The goal of Brain-Computer Interfaces is to translate a user's brain activity into commands. To achieve this, the subject is equipped with sensors on their scalp that each record the electrical signals from a certain area of their brain using Electroencephalography (EEG). This EEG is a multivar...
Saved in:
Main Author: | Thibault de Surrel |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-03-01
|
Series: | Science Talks |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2772569325000040 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds
by: Joarder Jafor Sadique, et al.
Published: (2025-01-01) -
Classification of Buried Objects From Ground Penetrating Radar Images by Using Second-Order Deep Learning Models
by: Douba Jafuno, et al.
Published: (2025-01-01) -
A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain–Computer Interfaces to Enhance Motor Imagery Classification
by: Souheyl Mallat, et al.
Published: (2025-01-01) -
Positioning accuracy improvement in high‐speed GPS receivers using sequential extended Kalman filter
by: Narges Rahemi, et al.
Published: (2021-06-01) -
Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline
by: Nalinda D. Liyanagedera, et al.
Published: (2025-02-01)