scholarly journals Corrigendum to “Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation”

2017 ◽  
Vol 2017 ◽  
pp. 1-1 ◽  
Author(s):  
Ju-Chi Liu ◽  
Hung-Chyun Chou ◽  
Chien-Hsiu Chen ◽  
Yi-Tseng Lin ◽  
Chung-Hsien Kuo
Keyword(s):  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Time Shift ◽  
Correlation Algorithm ◽  
P300 Event Related Potential

scholarly journals Time-Shift Correlation Algorithm for P300 Event Related Potential Brain-Computer Interface Implementation

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Ju-Chi Liu ◽  
Hung-Chyun Chou ◽  
Chien-Hsiu Chen ◽  
Yi-Tseng Lin ◽  
Chung-Hsien Kuo
Keyword(s):  
Humanoid Robot ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Time Shift ◽  
Series Data ◽  
Adult Size ◽  
Accuracy Rate ◽  
Correlation Algorithm ◽  
High Efficient

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI). The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node. Two operating modes, including fast-recognition mode (FM) and accuracy-recognition mode (AM), were realized. The proposed BCI system was implemented on an embedded system for commanding an adult-size humanoid robot to evaluate the performance from investigating the ground truth trajectories of the humanoid robot. When the humanoid robot walked in a spacious area, the FM was used to control the robot with a higher information transfer rate (ITR). When the robot walked in a crowded area, the AM was used for high accuracy of recognition to reduce the risk of collision. The experimental results showed that, in 100 trials, the accuracy rate of FM was 87.8% and the average ITR was 52.73 bits/min. In addition, the accuracy rate was improved to 92% for the AM, and the average ITR decreased to 31.27 bits/min. due to strict recognition constraints.

scholarly journals Effect of Distracting Background Speech in an Auditory Brain–Computer Interface

2021 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Álvaro Fernández-Rodríguez ◽  
Ricardo Ron-Angevin ◽  
Ernesto J. Sanz-Arigita ◽  
Antoine Parize ◽  
Juliette Esquirol ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Cognitive Workload ◽  
Auditory Event ◽  
Shared Attention ◽  
Attentional Condition ◽  
Different Types ◽  
The Impact ◽  
Auditory Bci

Studies so far have analyzed the effect of distractor stimuli in different types of brain–computer interface (BCI). However, the effect of a background speech has not been studied using an auditory event-related potential (ERP-BCI), a convenient option when the visual path cannot be adopted by users. Thus, the aim of the present work is to examine the impact of a background speech on selection performance and user workload in auditory BCI systems. Eleven participants tested three conditions: (i) auditory BCI control condition, (ii) auditory BCI with a background speech to ignore (non-attentional condition), and (iii) auditory BCI while the user has to pay attention to the background speech (attentional condition). The results demonstrated that, despite no significant differences in performance, shared attention to auditory BCI and background speech required a higher cognitive workload. In addition, the P300 target stimuli in the non-attentional condition were significantly higher than those in the attentional condition for several channels. The non-attentional condition was the only condition that showed significant differences in the amplitude of the P300 between target and non-target stimuli. The present study indicates that background speech, especially when it is attended to, is an important interference that should be avoided while using an auditory BCI.

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 Comparative analysis of two-group supervised classification algorithms in the study of P300-based brain-computer interface

2019 ◽  
Vol 252 ◽  
pp. 03010
Author(s):  
Małgorzata Plechawska-Wójcik ◽  
Monika Kaczorowska ◽  
Bernadetta Michalik
Keyword(s):  
Comparative Analysis ◽  
Supervised Classification ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Process Comparison ◽  
Eeg Data ◽  
Comparative Accuracy ◽  
Supervised Classification Methods

The main goal of the paper is to perform a comparative accuracy analysis of the two-group classification of EEG data collected during the P300-based brain-computer interface tests. The brain-computer interface is a technology that allows establishing communication between a human brain and external devices. BCIs may be applied in medicine to improve the life of disabled people and as well for entertainment. The P300 is an event-related potential (ERP) appearing about 300 ms after the occurrence of the stimulus of visual, auditory or sensory nature. It is based on the phenomenon observed in anticipation for a target event among non-target events. The 21-channel 201 Mitsar amplifier was used during the experiment to store EEG data from seven electrodes placed on the dedicated cap. The study was conducted on a group of five persons using P300 scenario available in OpenVibe software. The experiment was based on three steps the classifier learning process, comparison and averaging of the obtained result and the final test of the classifier. The comparative analysis was performed with the application of two supervised classification methods: Linear Discriminant Analysis (LDA) and Multi-layer Perceptron (MLP). The preliminary data analysis, extraction and feature selection was performed prior to the classification.

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