## 3 Results
### Cognitive Enhancement
Authors propose using BCI-VR to assess and train cognitive functions such as memory and attention through immersive VR tasks like virtual navigation and object recognition. The authors suggest that ChatGPT can personalize feedback mechanisms, potentially by adjusting task difficulty based on real-time BCI data and providing tailored encouragement to enhance user engagement. The article's findings suggest a novel approach to MCI classification by leveraging ChatGPT's ability to analyze diverse data sources. The authors propose that ChatGPT could integrate BCI data with patient medical history and subjective reports to enhance diagnostic accuracy. Additionally, ChatGPT's pattern recognition capabilities might uncover subtle brain activity patterns indicative of MCI. Figure 4 illustrates the dynamic interplay between the user, BCI system, and virtual environment. Our review highlights the potential of this closed-loop system to revolutionize fields like neurorehabilitation, cognitive assessment, and human-computer interaction. To provide a structured overview of the current state of knowledge in each key area, we have compiled a comprehensive table (Table 2) outlining the advancements, challenges, and future directions for each element of the BCI-VR system. However, realizing this potential requires overcoming challenges related to signal quality, user training, and ethical considerations. Addressing these limitations will pave the way for wider adoption of BCI-VR technology, unlocking its transformative power across diverse domains. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline
**Area** & **State of the Art** \\ \hline BCI Paradigms & Motor Imagery: Users imagine movements to generate brain signals. Widely used but requires training and has limitations in accuracy and speed; \\  & SSVEP: Users focus on flickering stimuli at specific frequencies. High accuracy and speed but can be visually fatiguing; \\  & P300: Users focus on a target stimulus within a flashing array. Requires minimal training but has lower information transfer rates; \\  & Hybrid Paradigms: Combining multiple paradigms to leverage their strengths and mitigate weaknesses is gaining traction. \\ \hline EEG Signal Acquisition & Electroencephalography: Non-invasive, portable, and affordable, making it dominant in BCI-VR research; \\  & High-Density EEG: Using more electrodes for improved spatial resolution and signal quality; Dry Electrodes: Enhancing user comfort and reducing setup time, though signal quality can be a concern. \\ \hline Feature Extraction & Time-Frequency Analysis: Wavelet Transform and Short-Time Fourier Transform are commonly used to extract relevant features from EEG signals; \\  & Spatial Filtering: Techniques like Common Spatial Patterns are used to enhance signal-to-noise ratio and extract spatially relevant features; \\  & Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks are increasingly used for automatic feature extraction and classification; \\ \hline Classification & Machine Learning: Support Vector Machines and Linear Discriminant Analysis are popular for classifying EEG patterns; \\  & Deep Learning: Deep Neural Networks, particularly CNNs and RNNs, are showing promise in achieving higher accuracy; \\  & Transfer Learning: Utilizing pre-trained models to reduce training time and improve performance is an active area of research. \\ \hline VR Simulation and Feedback & Realistic Environments: Creating immersive and engaging VR experiences to enhance user motivation and task performance; \\  & Adaptive Environments: Tailoring VR scenarios based on user performance and brain activity for personalized training and rehabilitation; \\  & Multimodal Feedback: Integrating visual, auditory, and haptic feedback to provide a richer and more intuitive user experience. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BCI-VR system components: current status, challenges, and future directions. Figure 4: Information flow in a closed-loop VR-BCI system.