Second Attention Workshop Report

This repository contains the reference implementation for the before/after EEG report for Second Attention Workshop participants. It is designed as a visual and UX guide for the development team building the app that processes EEG data and auto-generates participant reports.

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📊 Project Purpose

• Showcase: This is the intended output for the workshop's before/after comparison report.
• Reference: Use this as a design/UX and data-visualization reference for auto-generating reports from participant EEG data.

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🚀 Live Demo

https://second-attention-workshop-report.vercel.app/

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🎥 Loom Walkthrough

https://www.loom.com/share/57fbedec20634ef8ab7a66314f5f6f36?sid=669c30cf-2e25-4ae8-b1fe-ff0cb57d661b

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🛠️ How to Run Locally

1. Clone the repo:

   git clone https://github.com/Deducer/second-attention-workshop-report.git
   cd second-attention-workshop-report

2. Install dependencies and start the app:

   cd second-attention-app
   npm install
   npm start

3. Open http://localhost:3000 to view the report.

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🧠 What's Dynamic

• All numbers, bars, and graphs are to be generated from participant EEG data.
• The "before" and "after" states correspond to baseline and entrained meditation sessions.
• The report is designed to be auto-generated for each participant after their workshop session.

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📥 Data Requirements for Report Generation

For each section/element of the report, the following data is required (from EEG analysis and participant input):

1. Meditative Depth Score (Gradient Card)

• Inputs:
  • Aggregate meditative depth metric for each session (e.g., Alpha-Theta power ratio, or a custom score)
  • "Before" (baseline) and "After" (entrained) values
• How to compute:
  • Typically derived from band power analysis (e.g., mean Alpha+Theta power during meditation)

2. Brain Activity Comparison (Spatial EEG)

• Inputs:
  • Band power (Alpha, Theta, etc.) for each electrode/region (O1, O2, T3, T4) for both sessions
  • (Optional) Coherence/connectivity values between regions
• How to compute:
  • Compute average band power per region for each session
  • (Optional) Compute pairwise coherence between regions

3. Neural Coherence (Progress Bar)

• Inputs:
  • Global or average coherence value for each session (e.g., mean pairwise coherence across all regions)
• How to compute:
  • Use standard EEG coherence analysis (e.g., magnitude-squared coherence in Alpha/Theta bands)

4. Wave Metrics (Alpha, Theta, Beta)

• Inputs:
  • Mean band power for Alpha, Theta, Beta for each session (whole brain or specific regions)
• How to compute:
  • Compute mean power in each band for each session
  • Calculate % change (After vs. Before)

5. Subjective Experience (SMS)

• Inputs:
  • State Mindfulness Scale (SMS) scores: total and subscales (Mind Awareness, Body Awareness) for each session
  • Individual item scores for notable improvements
• How to compute:
  • Collect via participant self-report (questionnaire)
  • Calculate subscale and total scores for before/after

6. Unique Neural Strength

• Inputs:
  • Any standout EEG metric (e.g., exceptional increase in a specific band or region, or unique coherence pattern)
• How to compute:
  • Identify participant-specific outliers or strengths in the EEG data

7. Comparative Results

• Inputs:
  • Participant's metrics (depth, enhancement, time to deep state) compared to a reference group/distribution
• How to compute:
  • Place participant's results in context of group data (e.g., percentile rank)

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🎨 Design & UX Notes

• Color Mapping:
  • Blue → Purple: "After"/"Improvement"/"Higher is better"
  • Peach: "Before"/"Baseline"
• Section Structure:
  • Meditative Depth Score (gradient card)
  • Brain Activity Comparison (spatial EEG, coherence)
  • Neural Coherence (progress bar)
  • Wave Metrics (Alpha, Theta, Beta)
  • Subjective Experience (SMS)
  • Unique Neural Strength & Recommendations
  • Comparative Results
• Responsiveness:
  • Layout is designed for desktop/tablet, but can be adapted for mobile.
• Accessibility:
  • Color contrast and font sizes are chosen for readability.

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📝 Developer Context & Next Steps

• This repo is a reference implementation: use the structure, styles, and data-visualization logic as a guide for the production app.
• All data is currently static; in production, replace with dynamic values from EEG processing pipeline.
• See Loom walkthrough for intent, dynamic data mapping, and design rationale.
• For questions, contact the report designer or project lead.

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📂 Repo Structure

• second-attention-app/ — React app source code
• public/ — Static assets (images, logos)
• README.md — This file

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📣 Additional Notes

• If you deploy a live demo, update the link above.
• If you record a Loom walkthrough, add the link above.
• For any design/UX clarifications, refer to the Loom or contact the designer.

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