A General Framework of Brain-Computer Interface with Visualization and Virtual Reality Feedback

Sun, Gufei; Li, Kuangda; Li, Xiaoqiang; Zhang, Bofeng; Yuan, Shizhong; Wu, Gengfeng

doi:10.1109/dasc.2009.72

“…In [22] authors evaluate an online system that allows the user to interactively select different frequency components for different actions. In [23] the authors propose a framework for signal visualization in real time, allowing the researchers to analyze the signal while the test subject receives the feedback in a virtual reality (VR) environment. Ideas on using more engaging environments for online feedback were also proposed in [24,25].…”

Section: Related Workmentioning

confidence: 99%

Mental state space visualization for interactive modeling of personalized BCI control strategies

Kuzovkin

¹

,

Tretyakov

²

,

Uusberg

³

et al. 2020

View full text Add to dashboard Cite

Objective. Numerous studies in the area of BCI are focused on the search for a better experimental paradigm -a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are motor imagery, mental computations, etc. We propose a technique that instead allows the user to try different mental actions in the search for the ones that will work best. Approach. The system is based on a modification of the self-organizing map (SOM) algorithm and enables interactive communication between the user and the learning system through a visualization of user's mental state space. During the interaction with the system the user converges on the paradigm that is most efficient and intuitive for that particular user. Main results. Results of the two experiments, one allowing muscular activity, another permitting mental activity only, demonstrate soundness of the proposed method and offer preliminary validation of the performance improvement over the traditional closed-loop feedback approach. Significance. The proposed method allows a user to visually explore their mental state space in real time, opening new opportunities for scientific inquiry. The application of this method to the area of brain-computer interfaces enables more efficient search for the mental states that will allow a user to reliably control a BCI system.

show abstract

“…This class represents an intention of the BCI user. The key step for identifying neurophysiological signals in a BCI is translating the features into commands [ 40 ]. In order to achieve this step, one can use either regression algorithms or classification algorithms, the classification algorithms being by far the most used in the BCI community [ 41 , 42 ].…”

Section: Typical Architecture Of Bci Systemsmentioning

confidence: 99%

A Prototype SSVEP Based Real Time BCI Gaming System

Martišius

¹

,

Damaševičius

²

2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

show abstract

Mental state space visualization for interactive modeling of personalized BCI control strategies

Kuzovkin

¹

,

Tretyakov

²

,

Uusberg

³

et al. 2019

Preprint

0

View full text Add to dashboard Cite

Objective. Numerous studies in the area of BCI are focused on the search for a better experimental paradigm -a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are motor imagery, mental computations, etc. We propose a technique that instead allows the user to try different mental actions in the search for the ones that will work best. Approach. The system is based on a modification of the self-organizing map (SOM) algorithm and enables interactive communication between the user and the learning system through a visualization of user's mental state space. During the interaction with the system the user converges on the paradigm that is most efficient and intuitive for that particular user. Main results. Results of the two experiments, one allowing muscular activity, another permitting mental activity only, demonstrate soundness of the proposed method and offer preliminary validation of the performance improvement over the traditional closed-loop feedback approach. Significance. The proposed method allows a user to visually explore their mental state space in real time, opening new opportunities for scientific inquiry. The application of this method to the area of brain-computer interfaces enables more efficient search for the mental states that will allow a user to reliably control a BCI system.

show abstract

A General Framework of Brain-Computer Interface with Visualization and Virtual Reality Feedback

Cited by 5 publications

References 13 publications

Mental state space visualization for interactive modeling of personalized BCI control strategies

Mental state space visualization for interactive modeling of personalized BCI control strategies

A Prototype SSVEP Based Real Time BCI Gaming System

Mental state space visualization for interactive modeling of personalized BCI control strategies

Contact Info

Product

Resources

About