Classification of Hand Movement Stages for Brain–Computer Interface Using Convolutional Neural Network

2018 ◽  
pp. 189-199
Author(s):  
Kriti Singhal ◽  
Evi Agarwal ◽  
Arpit Yadav ◽  
Anuraj Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Computer Interface ◽  
Hand Movement ◽  
Computer Interface

scholarly journals Classification of the gaze fixations in the eye-brain-computer interface paradigm with a compact convolutional neural network

2018 ◽  
Vol 145 ◽  
pp. 293-299
Author(s):  
Bogdan L. Kozyrskiy ◽  
Anastasia O. Ovchinnikova ◽  
Alena D. Moskalenko ◽  
Boris M. Velichkovsky ◽  
Sergei L. Shishkin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
The Gaze ◽  
Gaze Fixations

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

scholarly journals Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1199 ◽  
Author(s):  
Hyeon Kyu Lee ◽  
Young-Seok Choi
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Continuous Wavelet ◽  
Time Frequency

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

scholarly journals Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

2007 ◽  
Vol 2007 ◽  
pp. 1-14 ◽  
Author(s):  
Qibin Zhao ◽  
Liqing Zhang
Keyword(s):  
Brain Computer Interface ◽  
Hand Movement ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Single Trial ◽  
Spatial Filters ◽  
Classification Rate ◽  
Temporal Features ◽  
Related Potentials

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

scholarly journals Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jaehong Yoon ◽  
Jungnyun Lee ◽  
Mincheol Whang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Parietal Lobe ◽  
Brain Computer Interface ◽  
Occipital Lobe ◽  
Event Related Potential ◽  
Computer Interface ◽  
Feature Maps ◽  
Neural Activities ◽  
Temporal Features

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

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