Optimal channel and frequency band‐based feature selection for motor imagery electroencephalogram classification-Reference-Cited by-同舟云学术

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Optimal channel and frequency band‐based feature selection for motor imagery electroencephalogram classification

Published:2022-11-02 Issue:2 Volume:33 Page:670-679
ISSN:0899-9457
Container-title:International Journal of Imaging Systems and Technology
language:en
Short-container-title:Int J Imaging Syst Tech

Author:

Meng Ming¹^ORCID,Dong Zhichao¹,Gao Yunyuan¹,She Qingshan¹

Affiliation:

1. School of Automation Hangzhou Dianzi University Hangzhou China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22823

Reference35 articles.

1. Brain-computer interface technology: a review of the first international meeting

2. Brain Computer Interfaces, a Review

3. The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects

4. An Asynchronous Hybrid Spelling Approach Based on EEG–EOG Signals for Chinese Character Input

5. Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain–Computer Interface System;Sensors;2024-08-01

2. Comparative Analysis of Mental Workload in Real vs. Graphic Virtual Reality Environments: Insights from Eeg and Subjective Assessments;2024

3. The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets;Frontiers in Human Neuroscience;2023-06-05

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