Comparison of Domain Selection Methods for Multi-Source Manifold Feature Transfer Learning in Electroencephalogram Classification

Comparison of Domain Selection Methods for Multi-Source Manifold Feature Transfer Learning in Electroencephalogram Classification

Transfer learning (TL) utilizes knowledge from the source domain (SD) to enhance the classification rate in the target domain (TD). It has been widely used to address the challenge of sessional and inter-subject variations in electroencephalogram (EEG)-based brain–computer interfaces (BCIs). However...

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Bibliographic Details
Main Authors: Rito Clifford Maswanganyi, Chungling Tu, Pius Adewale Owolawi, Shengzhi Du
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Applied Sciences
Subjects:
brain–computer interface (BCI)
electroencephalogram (EEG)
transfer learning (TL)
negative transfer (NT)
multi-source manifold feature transfer learning (MMFT)
enhanced multi-class MMFT (EMC-MMFT)
Online Access:https://www.mdpi.com/2076-3417/14/6/2326

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