NeuroCognitive Study Assistant (formerly Focus Buddy)

NeuroCognitive Study Assistant is an innovative web application designed to optimize students' study sessions using Brain-Computer Interface (BCI) technology. By analyzing neural data, our tool provides insights to improve focus and academic performance.

Table of Contents

• Introduction
• Objectives
• Methodology
• Key Features
• Challenges
• Results and Accomplishments
• Future Development
• Team
• Acknowledgements

Introduction

In today's academic environment, effective studying remains a significant challenge for many students. NeuroCognitive Study Assistant addresses this issue by leveraging Brain-Computer Interface technology to provide data-driven insights into optimal study periods and focus levels.

Objectives

1. Enhance students' focus and academic performance
2. Provide a personalized and stress-reduced studying experience
3. Utilize BCI technology to identify optimal study times through neural data analysis

Methodology

Our approach involves three key phases:

1. Data Collection: Neural data is gathered using a BCI-equipped headband.
2. Algorithm Development: Custom algorithms process and analyze the collected neural data.
3. Web Application Development: A user-friendly interface visualizes focus data and provides actionable insights.

Key Features

• Real-time neural data collection and analysis
• Visualization of focus levels over study periods
• Identification of optimal study times based on neural activity
• User-friendly web interface for data interpretation

Challenges

1. Technical Limitations:

   • BCI headband compatibility limited to Android devices
   • Time constraints prevented implementation of personalized machine learning algorithms

2. Data Handling:

   • Complex processing of neural data for accurate focus predictions
   • Integration of processed data into the web application

Results and Accomplishments

• Successfully collected and processed neural data using BCI technology
• Developed robust algorithms for handling data anomalies
• Created a functional web application for visualizing focus data
• Established an effective collaborative team environment

Future Development

1. Enhanced Focus Reports: Display topics with low focus percentages for targeted review
2. User Interface Improvements: Enhance usability and overall user experience
3. Customization Options: Implement personalized backgrounds and designs for increased engagement
4. Machine Learning Integration: Develop personalized algorithms for individual focus activities

Team

Our project was made possible by the collaborative efforts of:

• Coral Miniel
• Leon Kipkoech
• Kristian Vazquez
• Maria Camila Copo Amador

Acknowledgements

We extend our gratitude to all team members and mentors who contributed their expertise and support to this project.

Conclusion

NeuroCognitive Study Assistant demonstrates the potential of BCI technology in educational applications, paving the way for future innovations in personalized learning and cognitive enhancement.