VREEG

Dear Colleagues,

The concept of VR, Virtual Reality (VR), has always been described as a revolutionary innovation, basically for people to interact with computers. In fact, this innovation comes from the concept of presence, which is the essence of VR. This concept has enormous implications for how users interact with computer objects in the virtual world. The most common form of interaction in the virtual environment is locomotion. The term locomotion is used to indicate a user's motion control throughout the virtual environment. The project proposes the analysis and analysis of locomotion techniques, virtual reality technology and advanced biometric signal processing methods for BCI designs, as well as measurements from subconscious brain dynamics such as performance, cyber-discomfort, presence, usability and comfort. In this context, various locomotion techniques will be performed in a pre-designed virtual environment within certain tasks in the study to be carried out with at least 20 participants. As the outputs obtained from the study can be applied to VR software to be prepared in the fields of education, health, games, etc., it will also benefit the production of software for distance education, therapy, and serious games. In addition, the data set to be obtained from the study can be used as a benchmark data set. Especially in the VR domain, a dataset containing both event-related potential (ERP) and P300 signals can be used to evaluate BCI performance, compare and design new system diagrams without collecting any new data.

How to use

If you are interested in using this dataset, you will have to print, sign and scan an EULA (End User License Agreement) and upload it via the dataset request form. We will then supply you with a username and password to download the data. Please head on over to the downloads page for more details. Also, please consult the dataset description page for a complete explanation of the dataset.

Credits

All this would not have been possible without their valuable contribution and expert guidance, the project advisor:

Yaşar Daşdemir

Finally, we would like to thank our funding institutions:

Erzurum Technical University Scientific Research and Publication Ethics Committee
This study is funded by ETU Scientific Research Projects Coordination Unit with the project numbered 2021/012 titled "Examination of VR Locomotion Techniques for Brain-Computer Interface Designs with EEG Signals".

Dear Colleagues,

The concept of VR, Virtual Reality (VR), has always been described as a revolutionary innovation, basically for people to interact with computers. In fact, this innovation comes from the concept of presence, which is the essence of VR. This concept has enormous implications for how users interact with computer objects in the virtual world. The most common form of interaction in the virtual environment is locomotion. The term locomotion is used to indicate a user's motion control throughout the virtual environment. The project proposes the analysis and analysis of locomotion techniques, virtual reality technology and advanced biometric signal processing methods for BCI designs, as well as measurements from subconscious brain dynamics such as performance, cyber-discomfort, presence, usability and comfort. In this context, various locomotion techniques will be performed in a pre-designed virtual environment within certain tasks in the study to be carried out with at least 20 participants. As the outputs obtained from the study can be applied to VR software to be prepared in the fields of education, health, games, etc., it will also benefit the production of software for distance education, therapy, and serious games. In addition, the data set to be obtained from the study can be used as a benchmark data set. Especially in the VR domain, a dataset containing both event-related potential (ERP) and P300 signals can be used to evaluate BCI performance, compare and design new system diagrams without collecting any new data.

How to use

If you are interested in using this dataset, you will have to print, sign and scan an EULA (End User License Agreement) and upload it via the dataset request form. We will then supply you with a username and password to download the data. Please head on over to the downloads page for more details. Also, please consult the dataset description page for a complete explanation of the dataset.

Credits

All this would not have been possible without their valuable contribution and expert guidance, the project advisor:

Yaşar Daşdemir

Finally, we would like to thank our funding institutions:

Erzurum Technical University Scientific Research and Publication Ethics Committee
This study is funded by ETU Scientific Research Projects Coordination Unit with the project numbered 2021/012 titled "Examination of VR Locomotion Techniques for Brain-Computer Interface Designs with EEG Signals".

VREEG References

The dataset is made publicly available. We encourage other researchers to use this dataset, which presents VR-supported stimuli with their machine-learning methods. The dataset was first introduced in the following paper. If you feel that the dataset is helpful for your study, please add the following references to your publications.

VREEG Dataset paper:
Daşdemir, Y. (2023). Locomotion techniques with EEG signals in a virtual reality environment. Displays, 80, 102538.

VREMO References

The dataset is made publicly available. We encourage other researchers to use this dataset, which presents VR-supported stimuli with their machine-learning methods. The dataset was first introduced in the following paper. If you feel that the dataset is helpful for your study, please add the following references to your publications.

VREMO Dataset paper:
Daşdemir, Y. (2023). Classification of Emotional and Immersive Outcomes in the Context of Virtual Reality Scene Interactions. Diagnostics, 13(22), 3437.