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Application of virtual simulation situational model in Russian spatial preposition teaching.

Yanrong Gao^1,2, R T Kassymova¹, Yong Luo³

¹Faculty of Philology and World Languages, Al-Farabi Kazakh Nation University, Almaty, Kazakhstan.

Frontiers in Psychology

|October 3, 2022

Summary

This study introduces a novel Brain-Computer Interface (BCI) system using Steady State Visual Evoked Potential (SSVEP) and Hybrid Frequency-Phase Modulation (HFPM) to enhance Russian spatial preposition teaching via 3D Virtual Simulation (VS). The system achieves high accuracy and information transfer rates.

Keywords:

Russian teaching SSVEP-BCI situation simulation spatial preposition virtual simulation

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Area of Science:

Educational Technology
Neuroscience
Human-Computer Interaction

Background:

Traditional language teaching methods can be enhanced by immersive technologies.
3D Virtual Simulation (VS) offers realistic environments for complex language concepts like Russian spatial prepositions.
Brain-Computer Interfaces (BCI) provide novel interaction methods for educational tools.

Purpose of the Study:

To improve the quality of teaching Russian spatial prepositions in colleges.
To explore key technologies for 3D Virtual Simulation (VS) teaching using Russian spatial prepositions as a case study.
To develop and evaluate a novel SSVEP-BCI system for enhanced VS language learning.

Main Methods:

Construction of a 3D VS situational teaching system focused on Human-Computer Interaction (HCI).
Development of a Brain-Computer Interface (BCI) controlled by Steady State Visual Evoked Potential (SSVEP).
Implementation of a Hybrid Frequency-Phase Modulation (HFPM) technique for SSVEP signal processing and recognition.

Main Results:

The developed SSVEP-BCI system demonstrated increasing recognition accuracy with longer data lengths.
The highest Information Transfer Rate (ITR) achieved was 242.21 ± 46.88 bits/min with a data length of 0.6 seconds.
A high-speed BCI character input system based on SSVEP and HFPM was successfully designed.

Conclusions:

The proposed SSVEP-BCI system based on HFPM offers superior performance compared to existing BCI character input systems.
This BCI system holds significant value for optimizing virtual simulation systems in language education.
The integration of SSVEP-BCI technology enhances the realism and effectiveness of VS for teaching Russian spatial prepositions.