Update README.md

README.md

@@ -31,9 +31,9 @@ size_categories:
 
 *UNDER DEVELOPMENT for TESTING*
 
-*made using app in the /generator/ folder which is under development testing*
+*This dataset was made using app in the /generator/ folder which is under development testing.*
 
-# Use Cases ideas and concepts: 
+## Use case ideas and concepts: 
 
 BCI Intent Data Study and Testing (conceptual early design)
 
@@ -53,7 +53,7 @@ Validating algorithms in frontier labs (e.g., Neuralink, Paradromics) for high-d
 
 This dataset contains high-bandwidth neural training data collected from BCI-FPS, a specialized training platform for brain-computer interface research.
 
-### Dataset Summary
+## Dataset Summary
 
 - **Training Mode**: MOTOR IMAGERY
 - **Session ID**: bci_fps_motor_imagery_1767171179245
@@ -62,20 +62,18 @@ This dataset contains high-bandwidth neural training data collected from BCI-FPS
 - **Neural Channels**: 32
 - **Data Points**: 11,314
 
-### Supported Tasks
+## Supported Tasks
 
 - **Motor Imagery Training for prosthetic control**
 - **Neural Decoding**: Training models to decode user intent from neural signals
 - **BCI Calibration**: Providing ground truth data for BCI system calibration
 - **Disability Research**: Supporting development of assistive technologies
 
-### Languages
+## Languages
 
 English (interface and documentation)
 
-## Dataset Structure
-
-### Data Instances
+## Data Instances
 
 ```json
 {
@@ -133,32 +131,28 @@ English (interface and documentation)
 }
 ```
 
-### Data Fields
+## Data Fields
 
 See `metadata.json` for complete schema documentation.
 
-## Dataset Creation
-
-### Source Data
+## Source Data
 
 - **Platform**: Web-based BCI-FPS Training Environment
 - **Sampling Rate**: 1000 Hz
 - **Collection Method**: Real-time telemetry during BCI training tasks
 - **Neural Simulation**: Synthetic neural data representing ideal BCI signals
 
-### Annotations
+## Annotations
 
 - **Annotation process**: Automatic intent labeling during gameplay
 - **Annotation types**: Motor imagery, visual stimuli, handwriting intent
 - **Who annotated**: System automatically labels based on game state
 
-### Personal and Sensitive Information
+## Personal and Sensitive Information
 
 No personal information is collected. All data is synthetic/anonymous.
 
-## Considerations for Using the Data
-
-### Social Impact
+## Social Impact
 
 This dataset enables research in:
 - Neuralink-style brain-computer interfaces
@@ -166,11 +160,11 @@ This dataset enables research in:
 - Human-AI interaction systems
 - Neural decoding algorithms
 
-### Discussion of Biases
+## Discussion of Biases
 
 Synthetic neural data may not perfectly represent biological signals. Results should be validated with real neural recordings.
 
-### Other Known Limitations
+## Other Known Limitations
 
 - Simulated neural signals
 - Idealized game environment
@@ -178,7 +172,7 @@ Synthetic neural data may not perfectly represent biological signals. Results sh
 
 ## Additional Information
 
-### Dataset Curators
+## Dataset Curators
 
 huggingface.co/webXOS
 
@@ -186,13 +180,13 @@ huggingface.co/webXOS
 
 MIT License
 
-### Citation Information
+## Citation Information
 
 ```bibtex
-@misc{bci_fps_motor_imagery_2024,
-  title={BCI-FPS motor_imagery Training Dataset},
-  author={Neuralink Research},
+@misc{bci_fps_motor_imagery,
+  title={BCI-FPS motor_imagery Dataset},
+  author={webXOS,
   year={2024},
-  note={High-frequency intent decoding data for brain-computer interface development}
+  note={for testing and development purposes}
 }
 ```