Closing the loop in cortically-coupled computer vision: a brain–computer interface for searching image databases

2011 ◽  
Vol 8 (3) ◽  
pp. 036025 ◽  
Author(s):  
Eric A Pohlmeyer ◽  
Jun Wang ◽  
David C Jangraw ◽  
Bin Lou ◽  
Shih-Fu Chang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Brain Computer Interface ◽  
Image Databases ◽  
Computer Interface ◽  
Closing The Loop

A Speedy Brain-Computer Interface Combined with Computer Vision

2021 ◽  
pp. 128-134
Author(s):  
Kaixuan Liu ◽  
Yang Yu ◽  
Yadong Liu ◽  
Zongtan Zhou
Keyword(s):  
Computer Vision ◽  
Brain Computer Interface ◽  
Computer Interface

scholarly journals Feasibility of a Hybrid Brain-Computer Interface for Advanced Functional Electrical Therapy

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Andrej M. Savić ◽  
Nebojša M. Malešević ◽  
Mirjana B. Popović
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Second Stage ◽  
Electrical Therapy ◽  
Closing The Loop ◽  
The Will ◽  
Two Stages ◽  
Impaired Motor Function ◽  
Stimulation Pattern ◽  
Bci System

We present a feasibility study of a novel hybrid brain-computer interface (BCI) system for advanced functional electrical therapy (FET) of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP) and event-related desynchronization (ERD). The sequence of the two stages, SSVEP-BCI and ERD-BCI, runs in a closed-loop architecture. The first stage, SSVEP-BCI, acts as a selector of electrical stimulation pattern that corresponds to one of the three basic types of grasp: palmar, lateral, or precision. In the second stage, ERD-BCI operates as a brain switch which activates the stimulation pattern selected in the previous stage. The system was tested in 6 healthy subjects who were all able to control the device with accuracy in a range of 0.64–0.96. The results provided the reference data needed for the planned clinical study. This novel BCI may promote further restoration of the impaired motor function by closing the loop between the “will to move” and contingent temporally synchronized sensory feedback.

Shared control of a robotic arm using non-invasive brain–computer interface and computer vision guidance

2019 ◽  
Vol 115 ◽  
pp. 121-129 ◽  
Author(s):  
Yang Xu ◽  
Cheng Ding ◽  
Xiaokang Shu ◽  
Kai Gui ◽  
Yulia Bezsudnova ◽  
...  
Keyword(s):  
Computer Vision ◽  
Brain Computer Interface ◽  
Shared Control ◽  
Computer Interface ◽  
Robotic Arm ◽  
Non Invasive ◽  
Vision Guidance

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

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

Sign in / Sign up

Export Citation Format

Share Document