A novel method of motor imagery classification using eeg signal

作者：

Highlights：

• A Braisn-Computer Interface (BCI) framework depend on motor imagery (MI) recognizes templates of electrical cerebrum movement.

• Principal Component Analysis (PCA) is used toward to minimize the high dimensionality raw facts into lower dimensional data.

• BCI competition dataset III is used in proposed work.

摘要

•A Braisn-Computer Interface (BCI) framework depend on motor imagery (MI) recognizes templates of electrical cerebrum movement.•Principal Component Analysis (PCA) is used toward to minimize the high dimensionality raw facts into lower dimensional data.•BCI competition dataset III is used in proposed work.

论文关键词：Electroencephalogram,BCI,Principal component analysis,ELM,Fisher’s linear discriminant

论文评审过程：Received 18 June 2019, Revised 5 November 2019, Accepted 30 December 2019, Available online 31 December 2019, Version of Record 8 January 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2019.101787