Journal
NEURAL COMPUTING & APPLICATIONS
Volume 32, Issue 11, Pages 6601-6609Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-018-3735-3
Keywords
Brain-computer interface; Electroencephalogram; Motor imagery; Extreme learning machine; Sparse Bayesian learning
Categories
Funding
- National Natural Science Foundation of China [61673124]
Ask authors/readers for more resources
Mu rhythm is a spontaneous neural response occurring during a motor imagery (MI) task and has been increasingly applied to the design of brain-computer interface (BCI). Accurate classification of MI is usually rather difficult to be achieved since mu rhythm is very weak and likely to be contaminated by other background noises. As an extension of the single layer feedforward network, extreme learning machine (ELM) has recently proven to be more efficient than support vector machine that is a benchmark for MI-related EEG classification. With probabilistic inference, this study introduces a sparse Bayesian ELM (SBELM)-based algorithm to improve the classification performance of MI. SBELM is able to automatically control the model complexity and exclude redundant hidden neurons by combining advantageous of both ELM and sparse Bayesian learning. The effectiveness of SBELM for MI-related EEG classification is validated on a public dataset from BCI Competition IV IIb in comparison with several other competing algorithms. Superior classification accuracy confirms that the proposed SBELM-based algorithm is a promising candidate for performance improvement of an MI BCI.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available