A Novel Synchronous Hybrid Steady-State Brain-Computer Interface Based on Visual and Auditory Integration **This work was supported by the National Key Research & Development Program of China (2017YFA0701103), the Open Funding Project of National Key Laboratory of Human Factors Engineering (SYFD061903K and SYFD160051806), the National Natural Science Foundation of China (U1913601, 61773364 & 81927804), the Foundation of the Key Laboratory for Equipment Advanced Research (6142222200209 and 6142222180204), the Foundation Project in the field of Equipment Advanced Research (61400020402), the CAS Youth Innovation Promotion Association (2018395), the Shenzhen Basic Research Program (JCYJ20170818163724754 & JCYJ20200109114805984), and the Shenzhen Engineering Laboratory of Neural Rehabilitation Technology.

2021 ◽  
Author(s):  
Jun Xie ◽  
Zhiyuan Ren ◽  
Yi Liu ◽  
Peng Fang ◽  
Guanglin Li ◽  
...  
Keyword(s):  
Steady State ◽  
Research Program ◽  
Brain Computer Interface ◽  
Development Program ◽  
Basic Research ◽  
Computer Interface ◽  
Human Factors Engineering ◽  
Research Development ◽  
Rehabilitation Technology ◽  
Auditory Integration

scholarly journals Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery

2020 ◽  
Author(s):  
Lujia Zhou ◽  
Xuewen Tao ◽  
Feng He ◽  
Peng Zhou ◽  
Hongzhi Qi
Keyword(s):  
Steady State ◽  
Motor Imagery ◽  
Stroke Rehabilitation ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Recognition Rate ◽  
Computer Interface ◽  
Task Specificity ◽  
Rehabilitation Technology ◽  
The Brain

Abstract Background: In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). Methods: In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. Results: The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p<0.05), but the sensitivity was not significantly different. Conclusions: These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training.

Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Stanisław Karkosz ◽  
Marcin Jukiewicz
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Steady State ◽  
Brain Computer Interface ◽  
Morphological Features ◽  
Computer Interface ◽  
Visually Evoked Potentials ◽  
Computer Interfaces ◽  
Subject Variability ◽  
Selection For

AbstractObjectivesOptimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.MethodsSystem of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).ResultsThe designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.ConclusionsIt is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.

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