EEG classification using sparse Bayesian extreme learning machine for brain–computer interface

Zhichao, Jin; Zhou, Guoxu; Gao, Daqi; Yu, Zhang

doi:10.1007/s00521-018-3735-3

Cited by 132 publications

(80 citation statements)

References 68 publications

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“…By optimizing an energy function based on prior gradient sparsity, the L0 gradient minimization model shows a decent ability to perform image de-noising and a strong ability to preserve sharp features [13] in respect to traditional image de-noising method, such as Gaussian filter and shock filter. Similar works are also found in [14,15].…”

Section: Related Worksupporting

confidence: 87%

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

2019

J Image Video Proc.

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A clear stele image of ancient Chinese calligraphy pieces is very useful for studying ancient Chinese calligraphy. However, due to hundreds of or even thousands of years of natural or artificial damage on stele, images of ancient Chinese stele calligraphy works usually suffer from a large amount of image noise, and which usually leads to a poor visibility. To address this problem, in this paper, we propose a de-noising method based on L0 gradient minimization and guided filter. It consists of two main operations in sequence: First, L0 gradient minimization is utilized to obtain a random-noise free map, and then the random-noise free map is used as a guided image, and convoluted with its corresponding original noised stele image by a guided filter to obtain an edge preserved random-noise free image. Finally, the eight-connection region-based de-noising technique is followed to remove ant-like isolated blocks. Experiments demonstrate that the proposed method is superior to several recent published stele image de-noising techniques in terms of preserving the character structures.

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Section: Related Worksupporting

confidence: 87%

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

2019

J Image Video Proc.

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“…At the same time, the brain is also an important organ of the human body, which has important research value. To facilitate the application of EEG signals in clinical studies and practical use, various studies have proposed the noninvasive brain-computer interface (BCI) technology [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Novel joint algorithm based on EEG in complex scenarios

Chen

Yang

Miao

et al. 2019

Computer Assisted Surgery

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At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

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“…In the near future, there will be no careers in a variety of fields, from driverless cars becoming commonplace, to personalroutine assistants, automatic response system (ARS) counsellors, and bank clerks. In the age of machines, it is only natural to let machines do the work [1][2][3][4][5], aiming for the operation principle of the machine and the direction of a machine's prediction. In this paper, we analyzed the principles of operation and prediction through a recurrent neural network (RNN) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Recurrent Neural Network and Predictions

Park

2020

Symmetry

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This paper analyzes the operation principle and predicted value of the recurrent-neural-network (RNN) structure, which is the most basic and suitable for the change of time in the structure of a neural network for various types of artificial intelligence (AI). In particular, an RNN in which all connections are symmetric guarantees that it will converge. The operating principle of a RNN is based on linear data combinations and is composed through the synthesis of nonlinear activation functions. Linear combined data are similar to the autoregressive-moving average (ARMA) method of statistical processing. However, distortion due to the nonlinear activation function in RNNs causes the predicted value to be different from the predicted ARMA value. Through this, we know the limit of the predicted value of an RNN and the range of prediction that changes according to the learning data. In addition to mathematical proofs, numerical experiments confirmed our claims.

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EEG classification using sparse Bayesian extreme learning machine for brain–computer interface

Cited by 132 publications

References 68 publications

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

Novel joint algorithm based on EEG in complex scenarios

Analysis of Recurrent Neural Network and Predictions

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