Research on the multi-classifier features of the motor imagery EEG signals in the brain computer interface

2018 ◽  
Author(s):  
Xiuwen Du ◽  
Zhuozheng Wang ◽  
Qiang Wu ◽  
Yingjie Dong
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
The Brain

Symmetrical feature for interpreting motor imagery EEG signals in the brain–computer interface

Optik ◽  
2017 ◽  
Vol 129 ◽  
pp. 163-171 ◽  
Author(s):  
Seung-Min Park ◽  
Xinyang Yu ◽  
Pharino Chum ◽  
Woo-Young Lee ◽  
Kwee-Bo Sim
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
The Brain

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.

Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

2021 ◽  
Vol 11 (12) ◽  
pp. 2918-2927
Author(s):  
A. Shankar ◽  
S. Muttan ◽  
D. Vaithiyanathan
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Discrete Wavelet ◽  
Feature Sets ◽  
Evaluation Of Performance ◽  
The Brain

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

The changes in the hemodynamic activity of the brain during motor imagery training with the use of brain-computer interface

2016 ◽  
Vol 42 (1) ◽  
pp. 1-12 ◽  
Author(s):  
A. A. Frolov ◽  
D. Husek ◽  
A. V. Silchenko ◽  
J. Tintera ◽  
J. Rydlo
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Imagery Training ◽  
The Brain

scholarly journals Application of Convolutional Neural Network Method in Brain Computer Interface

2021 ◽  
Vol 2078 (1) ◽  
pp. 012044
Author(s):  
Lingzhi Chen ◽  
Wei Deng ◽  
Chunjin Ji
Keyword(s):  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Network Method ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Bci System

Abstract Pattern Recognition is the most important part of the brain computer interface (BCI) system. More and more profound learning methods were applied in BCI to increase the overall quality of pattern recognition accuracy, especially in the BCI based on Electroencephalogram (EEG) signal. Convolutional Neural Networks (CNN) holds great promises, which has been extensively employed for feature classification in BCI. This paper will review the application of the CNN method in BCI based on various EEG signals.

scholarly journals Artifact Removal and of EEG Signal Classification for Brain Computer Interface (BCI) using Back Propagation

2020 ◽  
Vol 8 (6) ◽  
pp. 1275-1282
Keyword(s):  
Brain Computer Interface ◽  
Research Work ◽  
Back Propagation ◽  
Software Tool ◽  
Computer Interface ◽  
Eeg Signals ◽  
Biological Sources ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
The Brain

A brain-computer interface (BCI) provides a communication passage between the brain and an external stratagem. The Brain and its EEG signals are acquired from the BCI along its control signals and its widely used mechanism in the field of the biomedical fields. In this research work, an artifacts are removed algorithm in the EEG is developed and simulated in the MATLAB 2017a software tool. EEG signals from patients are recoded while recording some of the artificial signals added to it, which are instigated by using eye blinks, eye movement, muscle, and cardiac noise, and also non-biological sources. Using suitable filters these artificial signals can be removed. This paper aims to remove the artificial signals from EEG signals and parameters like mean, standard. Deviation are calculated and compared with other methods such as LAMICA and FASTERs. In the paper, it is also the proposed arrangement of EEG signals for the discovery of typical and anomalous exercises utilizing Wavelet change and Artificial Neural Network (ANN) Classifier is considered. Here, the framework utilizes the back proliferation with feed-forward for order which pursues the ANN grouping. Accuracy of the classification is calculated and compared with other states of art publications and found that it is better.

scholarly journals Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

2019 ◽  
Vol 3 (3) ◽  
pp. 80
Author(s):  
Muhammad Fawaz Saputra ◽  
Noor Akhmad Setiawan ◽  
Igi Ardiyanto
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Short Term Memory ◽  
Brain Computer Interface ◽  
Correction Method ◽  
Computer Interface ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Learning Methods ◽  
Motor Movements

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM.

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