scholarly journals Classifying BCI signals from novice users with extreme learning machine

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 494-500
Author(s):  
Germán Rodríguez-Bermúdez ◽  
Andrés Bueno-Crespo ◽  
F. José Martinez-Albaladejo
Keyword(s):  
Electrical Activity ◽  
Extreme Learning Machine ◽  
Brain Computer Interface ◽  
Experimental Results ◽  
Computer Interface ◽  
Classification Algorithms ◽  
Novice Users ◽  
Standard Classification ◽  
Learning Machine ◽  
The Brain

AbstractBrain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

scholarly journals Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingwei Zhang ◽  
Yao Hou ◽  
Rongnian Tang ◽  
Youjun Li
Keyword(s):  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Spatial Covariance ◽  
Learning Machine ◽  
Low Dimensional ◽  
Bci System

In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

scholarly journals Facial Expression Decoding based on fMRI Brain Signal

2019 ◽  
Vol 14 (4) ◽  
pp. 475-488
Author(s):  
Benchun Cao ◽  
Yanchun Liang ◽  
Shinichi Yoshida ◽  
Renchu Guan
Keyword(s):  
Support Vector Machine ◽  
Extreme Learning Machine ◽  
Facial Expressions ◽  
Classification Performance ◽  
Computer Interface ◽  
Support Vector ◽  
Brain Decoding ◽  
Learning Machine ◽  
The Brain ◽  
Better Than

The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.

An Auto-Contouring Method for Kidney Using a Novel Semi-Supervised Leaning Extreme Learning Machine Method

2021 ◽  
Vol 11 (8) ◽  
pp. 2198-2204
Author(s):  
Bo Li ◽  
Jing Xue ◽  
Kaijian Xia ◽  
Leyuan Zhou ◽  
Pengjiang Qian ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Extreme Learning Machine ◽  
Adaptive Radiation ◽  
Experimental Results ◽  
Radiotherapy Planning ◽  
Classification Algorithms ◽  
Adaptive Radiation Therapy ◽  
Data Set ◽  
Machine Method ◽  
Learning Machine

Background: Adaptive radiation therapy planning requires contour segmentation of dangerous organs in medical images. However, manual contour rendering is the most time-consuming and laborious work in radiotherapy planning. In order to solve this problem, we propose a novel semi-supervised leaning extreme learning machine (SSL-ELM) method to realize abdominal Magnetic Resonance Imaging (MRI) guided Adaptive Radiation Therapy (MR-ART) automatic contour rendering. Method/Material: Our algorithm is based on the assumption that data within the same class are close to each other. We use this heuristic method to improve the ELM algorithm. The experimental results show that our proposed method outperforms existing classification algorithms. We used a data set of eight patients with unresectable abdominal malignant tumors recruited by professionals approved by the Cleveland Medical Center Institution Review Board. Each group included MRI and airborne T1MRI with randomly selected treatment phases. Each MRI consisted of 16 slices with a resolution of 370 x 370 pixels. Manual and automatic contours of the kidney were compared using Dice similarity index (DSI). Results: The proposed SSL-ELM algorithm has better performance than most classification algorithms, and the experimental results also show that the DSI values are above 0.87, with some samples reaching 0.99.

Testing Extreme Learning Machine in Motor Imagery Brain Computer Interface

2017 ◽  
Vol 33 (5) ◽  
pp. 3103-3111
Author(s):  
Francisco J. Martínez-Albaladejo ◽  
Andrés Bueno-Crespo ◽  
Germán Rodríguez-Bermúdez
Keyword(s):  
Extreme Learning Machine ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Learning Machine

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

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