Simultaneously optimizing spatial spectral features based on mutual information for EEG classification

Jianjun Meng, Lin Yao, Xinjun Sheng, Dingguo Zhang, Xiangyang Zhu

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


High performance of the brain-computer interface (BCI) needs efficient algorithms to extract discriminative features from raw electroencephalography (EEG) signals. In this paper, we present a novel scheme to extract spatial spectral features for the motor imagery-based BCI. The learning task is formulated by maximizing the mutual information between spatial spectral features (MMISS) and class labels, by which a unique objective function directly related to Bayes classification error is optimized. The spatial spectral features are assumed to follow a parametric Gaussian distribution, which has been validated by the normal distribution Mardia's test, and under this assumption the estimation of mutual information is derived. We propose a gradient based alternative and iterative learning algorithm to optimize the cost function and derive the spatial and spectral filters simultaneously. The experimental results on dataset IVa of BCI competition III and dataset IIa of BCI competition IV show that the proposed MMISS is able to efficiently extract discriminative features from motor imagery-based EEG signals to enhance the classification accuracy compared to other existing algorithms.

Original languageEnglish (US)
Article number6871337
Pages (from-to)227-240
Number of pages14
JournalIEEE Transactions on Biomedical Engineering
Issue number1
StatePublished - Jan 1 2015

Bibliographical note

Publisher Copyright:
© 1964-2012 IEEE.


Dive into the research topics of 'Simultaneously optimizing spatial spectral features based on mutual information for EEG classification'. Together they form a unique fingerprint.

Cite this