Motor Imagery EEG Signals Decoding with Multi-view Weighted Features

Nan Li; Wangsen Li; Tingting Zhang; Dong Huang; Han Junfeng; Xiangzeng Kong

doi:10.1007/978-981-96-1904-7_31

ScienceGate Book Chapters

BOOK-CHAPTER

Motor Imagery EEG Signals Decoding with Multi-view Weighted Features

Nan Li Wangsen Li Tingting Zhang Dong Huang Han Junfeng Xiangzeng Kong

Year: 2025 Communications in computer and information science Pages: 338-349 Publisher: Springer Science+Business Media

DOI: 10.1007/978-981-96-1904-7_31

Get Full-Text PDF Get Analytical Report

Keywords:

Decoding methods Electroencephalography Motor imagery Computer science Artificial intelligence Speech recognition Pattern recognition (psychology) Computer vision Psychology Brain–computer interface Neuroscience Algorithm

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.09

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

EEG and Brain-Computer Interfaces

Life Sciences → Neuroscience → Cognitive Neuroscience

Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Blind Source Separation Techniques

Physical Sciences → Computer Science → Signal Processing

Motor Imagery EEG Signals Decoding with Multi-view Weighted Features

Metrics

Topics

Related Documents

Decoding Motor Imagery based EEG Signals

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals

A multi-view CNN encoding for motor imagery EEG signals

Efficient Multi-View Graph Convolutional Network with Self-Attention for Multi-Class Motor Imagery Decoding

Decoding of multi-modal signals for motor imagery based on window positioning