Modular Time-Frequency Joint Coding for a Virtual Keyboard Speller Using an SSVEP-Based Brain-Computer Interface

2021 ◽  
Author(s):  
Hui Yang ◽  
Wenli Lan ◽  
Jing He ◽  
Yue Leng ◽  
Ruimin Wang ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Time Frequency ◽  
Virtual Keyboard ◽  
Joint Coding

scholarly journals Virtual keyboard based on a brain-computer interface

2019 ◽  
Vol 514 ◽  
pp. 012020
Author(s):  
O A Rusanu ◽  
L Cristea ◽  
M C Luculescu ◽  
P A Cotfas ◽  
D T Cotfas
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Virtual Keyboard

scholarly journals Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shi Qiu ◽  
Junjun Li ◽  
Mengdi Cong ◽  
Chun Wu ◽  
Yan Qin ◽  
...  
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Pulmonary Nodules ◽  
Electrical Signal ◽  
Computer Interface ◽  
Solitary Pulmonary Nodules ◽  
Brain Response ◽  
Time Frequency ◽  
Image Presentation ◽  
The Brain

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

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.

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