Single channel brain-computer interface control system based on TGAM module

2017 ◽  
Author(s):  
Lu Zhang ◽  
Qingsong Lv ◽  
Yishen Xu
Keyword(s):  
Control System ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Interface Control

scholarly journals A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106826
Author(s):  
Giovanni Acampora ◽  
Pasquale Trinchese ◽  
Autilia Vitiello
Keyword(s):  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals

Pilot Study on Using Innovative Counting Peaks Method for Assessment Purposes of the EEG Data Recorded from a Single-Channel Non-Invasive Brain-Computer Interface

2021 ◽  
Author(s):  
Natalia Browarska ◽  
Jaroslaw Zygarlicki ◽  
Mariusz Pelc ◽  
Michal Niemczynowicz ◽  
Malgorzata Zygarlicka ◽  
...  
Keyword(s):  
Pilot Study ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Non Invasive ◽  
Eeg Data

scholarly journals Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game

2013 ◽  
Vol 5 (2) ◽  
pp. 117-128 ◽  
Author(s):  
Robert Leeb ◽  
Marcel Lancelle ◽  
Vera Kaiser ◽  
Dieter W. Fellner ◽  
Gert Pfurtscheller
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Interface Control

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.

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