Abstract
The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset.
Highlights
The ultimate goal of human computer interaction technology is to provide a natural and harmonious way to communicate with machines [1]
We propose two improved ELM (IELM) algorithms optimized by bionic whale optimization algorithms (WOA) for EEG classification of brain computer interface to better improve the ill-conditioned random single-hidden-layer feedforward neural networks (IRSLFN) problem of random initialization by introducing several better individuals voting strategies and different regularization mechanisms
The initial connection weights and bias between input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy and shrinking encircling mechanism in WOA-MLELM, and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix and promote the generalization performance and stability of the WOA-MLELM
Summary
The ultimate goal of human computer interaction technology is to provide a natural and harmonious way to communicate with machines [1]. We propose two IELM algorithms optimized by bionic WOA for EEG classification of brain computer interface to better improve the IRSLFN problem of random initialization by introducing several better individuals voting strategies and different regularization mechanisms. 2. We propose two IELM algorithms optimized by bionic WOA for EEG classification of brain computer interface to better improve the IRSLFN problem of the initially random set weights and biases by introducing several better individuals voting strategies and different regularization mechanisms. As the random initialization weights and biases and pseudo-inverse calculation mechanism of primeval ELM algorithms may result in an IRSLFN, we propose two IELM algorithms optimized by bionic WOA for EEG classification of brain computer interface to improve the IRSLFN problem. W (iter − 1) and b(iter − 1) are the weight matrix and bias respectively, which are randomly initialized and optimized by WOA. fen is the coding function from weights W i(iter) and biases bi(iter) to individuals Xind (iter − 1) in IWOA-ELM
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
