scholarly journals Phase-spatial beamforming renders a visual brain computer interface capable of exploiting EEG electrode phase shifts in motion-onset target responses

2021 ◽  
pp. 1-1
Author(s):  
Arno Libert ◽  
Benjamin Wittevrongel ◽  
Flavio Camarrone ◽  
Marc M. Van Hulle
Keyword(s):  
Brain Computer Interface ◽  
Phase Shifts ◽  
Computer Interface ◽  
Motion Onset ◽  
Onset Target

An Adaptive Motion-Onset VEP-Based Brain-Computer Interface

2015 ◽  
Vol 7 (4) ◽  
pp. 349-356 ◽  
Author(s):  
Rui Zhang ◽  
Peng Xu ◽  
Rui Chen ◽  
Teng Ma ◽  
Xulin Lv ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Motion Onset ◽  
Adaptive Motion

scholarly journals An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Teng Ma ◽  
Fali Li ◽  
Peiyang Li ◽  
Dezhong Yao ◽  
Yangsong Zhang ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Training Set ◽  
Motion Onset ◽  
Adaptive Calibration ◽  
Long Time ◽  
Updating Procedure ◽  
The Brain ◽  
Classifier Training

Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.

scholarly journals Visual Motion Onset Brain-Computer Interface

2016 ◽  
Author(s):  
Jair Peraira Junior ◽  
Caio Teixeira ◽  
Tomasz Rutkowski
Keyword(s):  
Classification Accuracy ◽  
Visual Motion ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Motion Onset ◽  
Motion Patterns ◽  
Linear Discriminant ◽  
Computer Screen ◽  
Computer Interfaces ◽  
Accuracy Comparison

The paper presents a study of two novel visual motion onset stimulus-based brain–computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event–related potential (ERP) paradigm allowing for “aha–responses” decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter–stimulus–interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non–significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no–eye–movements requiring visual BCIs.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information
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