Work

Many-to-Many Transfer Learning on Motor Imagery BCI

Year: 2024

Type: conference-paper

Abstract: This paper presents many-to-many domain adaptation strategy, named group learning, for motor imagery brain-computer interfaces (BCIs). Group learning, grounded in Riemannian geometry, simultaneously aligns multiple domains in a unified model, whereas fast alignment approach integrates new, unseen domains without re-estimating alignment matrices for all domains. Group learning creates a single machine learning model using data from previous subjects and/or sessions. Fast alignment utilizes the already trained model for an unseen domain without requiring any additional classifier training. The tests on five publicly available motor imagery databases demonstrate the robustness of group learning against negative learning. The classification accuracy scores of binary and multiclass databases show comparable, if not superior, performance to conventional subject-wise learning method.

Source: International Conference on Decision Aid Sciences and Applications (DASA)

DOI: 10.1109/dasa63652.2024.10836450

Authors: Fatih Altindiş, Bülent Yilmaz, Marco Congedo

Institutions: Abdullah Gül University, Gulf University for Science & Technology, Centre National de la Recherche Scientifique, GIPSA-Lab

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: High Fidelity Brain-Computer Interface for Motor Rehabilitation