How Many People Can Use a BCI System?

2014 ◽  
pp. 33-66 ◽  
Author(s):  
Günter Edlinger ◽  
Brendan Z. Allison ◽  
Christoph Guger
Keyword(s):  
Bci System

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 572
Author(s):  
Mads Jochumsen ◽  
Taha Al Muhammadee Janjua ◽  
Juan Carlos Arceo ◽  
Jimmy Lauber ◽  
Emilie Simoneau Buessinger ◽  
...  
Keyword(s):  
Open Source ◽  
Neural Plasticity ◽  
Motor Evoked Potentials ◽  
Low Cost ◽  
True Positive Rate ◽  
Positive Rate ◽  
3D Printed ◽  
Major Factors ◽  
And Control ◽  
Bci System

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

CNN–based classification of fNIRS signals in motor imagery BCI system

2021 ◽  
Author(s):  
Tengfei Ma ◽  
Shasha Wang ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
Julian Evans ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Bci System

EEG-based BCI system via arithmetic and emotional imagery

2014 ◽  
Author(s):  
Yunuong Punsawad ◽  
Juthamat Uengamphon ◽  
Yodchanan Wongsawat
Keyword(s):  
Emotional Imagery ◽  
Bci System

scholarly journals What if I had a third arm? An EEG study of a supernumerary BCI system

2019 ◽  
Author(s):  
Jaime A. Riascos ◽  
David Steeven Villa ◽  
Anderson Maciel ◽  
Luciana Nedel ◽  
Dante Barone
Keyword(s):  
Motor Imagery ◽  
Body Parts ◽  
Task Load ◽  
The Third ◽  
Person Perspective ◽  
Human Ability ◽  
Robotic Prosthesis ◽  
And Control ◽  
Bci System ◽  
Psychological Experiments

AbstractMotor imagery Brain-Computer Interface (MI-BCI) enables bodyless communication by means of the imagination of body movements. Since its apparition, MI-BCI has been widely used in applications such as guiding a robotic prosthesis, or the navigation in games and virtual reality (VR) environments. Although psychological experiments, such as the Rubber Hand Illusion - RHI, suggest the human ability for creating body transfer illusions, MI-BCI only uses the imagination of real body parts as neurofeedback training and control commands. The present work studies and explores the inclusion of an imaginary third arm as a part of the control commands for MI-BCI systems. It also compares the effectiveness of using the conventional arrows and fixation cross as training step (Graz condition) against realistic human hands performing the corresponding tasks from a first-person perspective (Hands condition); both conditions wearing a VR headset. Ten healthy subjects participated in a two-session EEG experiment involving open-close hand tasks, including a third arm that comes out from the chest. The EEG analysis shows a strong power decrease in the sensory-motor areas for the third arm task in both training conditions. Such activity is significantly stronger for Hands than Graz condition, suggesting that the realistic scenario can reduce the abstractness of the third arm and improve the generation of motor imagery signals. The cognitive load is also assessed both by NASA-TLX and Task Load index.

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