Update README.md

README.md

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 ## Description
 
-Time-synchronized multimodal dataset for BCI intent recognition, collected with frame-by-frame timelapse capture during FPS gameplay.
-
-A time-synchronized multimodal dataset hosted on Hugging Face for brain-computer interface (BCI) intent 
-ecognition research. It was collected via frame-by-frame timelapse capture during first-person shooter (FPS) gameplay and includes 
-synchronized image sequences (320x240 JPGs), game state data (like player position, velocity, ammo, and combat stats), BCI intent labels 
-across 13 categories, input data (mouse/keyboard), and RLHF ratings for combat and capture actions. The dataset is small, with 188 total 
-frames grouped into 1 temporal sequence from a 1:35-minute session, and a download size of about 685 kB. It's structured with JSONL files 
-for metadata and intents, a directory of images, and a sequences.json file for time-series analysis, making it suitable for deep learning 
-models like LSTMs or Transformers on multimodal temporal data. Tags include BCI, timelapse, FPS gameplay, intent recognition, multimodal, 
-time-series, RLHF, and sequence modeling.
+Time-synchronized multimodal dataset for BCI intent recognition, collected with frame-by-frame timelapse capture during FPS gameplay. 
+Hosted on Hugging Face for brain-computer interface (BCI) intent ecognition research. It was collected via frame-by-frame timelapse 
+capture during first-person shooter (FPS) gameplay and includes synchronized image sequences (320x240 JPGs), game state data (like 
+player position, velocity, ammo, and combat stats), BCI intent labels across 13 categories, input data (mouse/keyboard), and RLHF ratings 
+for combat and capture actions. The dataset is small, with 188 total frames grouped into 1 temporal sequence from a 1:35-minute session, 
+and a download size of about 685 kB. It's structured with JSONL files for metadata and intents, a directory of images, and a sequences.json 
+file for time-series analysis, making it suitable for deep learning models like LSTMs or Transformers on multimodal temporal data. Tags 
+include BCI, timelapse, FPS gameplay, intent recognition, multimodal, time-series, RLHF, and sequence modeling.
 
 ## Key Features
 - **Frame-by-Frame Timelapse**: Synchronized image sequences at 320x240 resolution