Balanced Graph-based regularized semi-supervised extreme learning machine for EEG classification
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ORIGINAL ARTICLE
Balanced Graph‑based regularized semi‑supervised extreme learning machine for EEG classification Qingshan She1,2 · Jie Zou1 · Ming Meng1 · Yingle Fan1 · Zhizeng Luo1 Received: 9 January 2020 / Accepted: 20 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Machine learning algorithms play a critical role in electroencephalograpy (EEG)-based brain-computer interface (BCI) systems. However, collecting labeled samples for classifier training and calibration is still difficult and time-consuming, especially for patients. As a promising alternative way to address the problem, semi-supervised learning has attracted much attention by exploiting both labeled and unlabeled samples in the training process. Nowadays, semi-supervised extreme learning machine (SS-ELM) is widely used in EEG classification due to its fast training speed and good generalization performance. However, the classification performance of SS-ELM largely depends on the quality of sample graph. The graphs of most semi-supervised algorithms are constructed by the similarity between labeled and unlabeled data called manifold graph. The more similar the structural information between samples, the greater probability they belong to the same class. In this paper, the label-consistency graph (LCG) and sample-similarity graph (SSG) are combined to constrain the model output. When the SSG is not accurate enough, the weight of LCG needs to be increased, and vice versa. The weight ratio of two graphs is optimized to obtain an optimal adjacency graph, and finally the best output weight vector is achieved. To verify the effectiveness of the proposed algorithm, it was validated and compared with several existing methods on two real datasets: BCI Competition IV Dataset 2a and BCI Competition III Dataset 4a. Experimental results show that our algorithm has achieved the promising results, especially when the number of labeled samples is small. Keywords Brain–computer interface · Electroencephalogram · Semi-supervised extreme learning machine · Labelconsistency graph · Sample-similarity graph
1 Introduction Brain-computer interface (BCI) is a communication and control system to enable the users interact with their surroundings without the involvement of peripheral nerves and muscles via brain activity [1]. Motor imagery (MI) is a typical paradigm in EEG-based BCI, wherein the mental imagination of movement is discriminated from EEG measurements and translated into control commands. It can greatly help the rehabilitation of the consciousness disorder and stroke patients, which can complete the required actions without performing any muscle activity [2]. Unfortunately, * Qingshan She [email protected] 1
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, Zhejiang, China
2
EEG signals have high non-stationarity and variability from a neuroscience perspective [3], which makes it diffi
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