IEEE Computational Intelligence Magazine - November 2022 - 16
Meta-Learning for Fast
and Privacy-Preserving
Source Knowledge Transfer
of EEG-Based BCIs
Siyang Li, Huanyu Wu, Lieyun Ding,
and Dongrui Wu
Huazhong University ofScience
and Technology, CHINA
Abstract-Electroencephalogram (EEG) based brain-computer
interfaces (BCIs) are used in many applications, due to their
low-risk, low-cost, and convenience. Because of EEG's high
variations across subjects and sessions, a long calibration session
is usually needed to adjust the system before each use, which is
time-consuming and user-unfriendly. Though various machine
learning approaches have been proposed to cope with this
problem, none of them considered individual differences,
data scarcity and data privacy simultaneously. In this paper, a
Digital Object Identifier 10.1109/MCI.2022.3199622
Date ofcurrent version: 3 November 2022
16 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2022
Corresponding author: Dongrui Wu (e-mail: drwu@hust.edu.cn).
1556-603X ß 2022 IEEE
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IEEE Computational Intelligence Magazine - November 2022
Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2022
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