Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere

Xu, Fangzhou; Zhou, Weidong; Zhen, Yilin; Yuan, Qi; Wu, Qi

doi:10.1142/s0129065716500222

Cited by 36 publications

(25 citation statements)

References 70 publications

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“…Development of BCI systems requires a multidisciplinary approach crossing many fields including neurobiology, psychology, engineering, mathematics, and computer science that is highly affected by developments in each of those fields (Mirzaei et al, ; Mirzaei and Adeli, ; Jiao et al, ). Classification of signals related to the mental simulation of actions, referred to as motor imagery (MI; Xu et al ; Zhang et al ; Liu et al, ), is a key step in BCI applications, for example, controlling devices by imagining their motions (Porcaro et al, ). A variety of neural signals recorded both noninvasively and invasively has been widely exploited in MI–BCI systems (Schudlo and Chau ; Li et al, ; Yang et al ; Shin and Im ).…”

Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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An important subfield of brain–computer interface is the classification of motor imagery (MI) signals where a presumed action, for example, imagining the hands' motions, is mentally simulated. The brain dynamics of MI is usually measured by electroencephalography (EEG) due to its noninvasiveness. The next generation of brain–computer interface systems can benefit from the generative deep learning (GDL) models by providing end‐to‐end (e2e) machine learning and increasing their accuracy. In this study, to exploit the e2e‐property of deep learning models, a novel GDL methodology is proposed where only minimal objective‐free preprocessing steps are needed. Furthermore, to deal with the complicated multi‐class MI–EEG signals, an innovative multilevel GDL‐based classifying scheme is proposed. The effectiveness of the proposed model and its robustness against noisy MI–EEG signals is evaluated using two different GDL models, that is, deep belief network and stacked sparse autoencoder in e2e manner. Experimental results demonstrate the effectiveness of the proposed methodology with improved accuracy compared with the widely used filter bank common spatial patterns algorithm.

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Section: Introductionmentioning

confidence: 99%

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

et al. 2019

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“…By contrast, our scheme achieves 99% accuracy with less than 10.5% features, which proves the effectiveness of the scheme proposed in the present study. Xu et al [21] proposed using gradient boosting to classify brain signals by extracting the combined features of fractal measures and LBP operators; 41 channels with the highest precision were selected, yielding 95% accuracy. Zhao et al [22] used band power for channel selection and feature extraction.…”

Section: Comparison Of the Classification Performancementioning

confidence: 99%

Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization

Ding

et al. 2020

Computational Intelligence and Neuroscience

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Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. However, irrelevant channels and misleading features unrelated to tasks limit classification performance. To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection. Modified Stockwell transforms were used for a feature extraction, and multilevel hybrid PSO-Bayesian linear discriminant analysis was applied to optimization and classification. The BCI Competition III dataset I was used here to confirm the superiority of the proposed scheme. Compared to a method without optimization (89% accuracy), the best classification accuracy of the PSO-based scheme was 99% when less than 10.5% of the original features were used, the test time was reduced by more than 90%, and it achieved Kappa values and F-score of 0.98 and 98.99%, respectively, and better signal-to-noise ratio, thereby outperforming existing algorithms. The results show that the channel and feature selection scheme can accelerate the speed of convergence to the global optimum and reduce the training time. As the proposed framework can significantly improve classification performance, effectively reduce the number of features, and greatly shorten the test time, it can serve as a reference for related real-time BCI application system research.

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“…A chaotic map shows some kind of chaotic behavior based on an iterative evolution function (Adeli & Jiang, ). Chaotic maps are often associated with fractals (Adeli, Ghosh‐Dastidar, & Dadmehr, ; Ghosh‐Dastidar, Adeli, & Dadmehr, ; Zhang & Zhou, ; Xu, Zhou, Zhen, Yuan, & Wu, ). Using a chaotic sequence, Alatas () proposed the following equation for the next position of each individual in the BC phase:

x_{i} ((),, t + 1) = x_{c} ((),, t) \pm \frac{α ((),, t) ((),, x_{\max} - x_{\min})}{t}

where α ( t ) with 0 < α ( t ) < 1 is a chaotic map defined by

α (t) = c \cdot f [α (t - 1)]

where c is a constant.…”

Section: Big Bang–big Crunch Searchmentioning

confidence: 99%

“…A chaotic map shows some kind of chaotic behavior based on an iterative evolution function (Adeli & Jiang, 2009). Chaotic maps are often associated with fractals Zhang & Zhou, 2015;Xu, Zhou, Zhen, Yuan, & Wu, 2016). Using a chaotic sequence, Alatas (2011) proposed the following equation for the next position of each individual in the BC phase:…”

Section: Algorithmmentioning

confidence: 99%

Physics‐based search and optimization: Inspirations from nature

Siddique

Adeli

2016

Expert Systems

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Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere

Cited by 36 publications

References 70 publications

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization

Physics‐based search and optimization: Inspirations from nature

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