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EEG Signal Classification & Doctor Dashboard

An explainable diagnostic dashboard that classifies EEG signals and presents trustworthy predictions to clinicians.

Overview

Several ML architectures are trained and the best is selected automatically, with SHAP and LIME making each prediction clinically interpretable. Reports are secured through Hyperledger-based SHA-256 hashing that detects tampering, and detailed PDFs are generated with room to expand into depression, anxiety and OCD screening.

Problem Statement

Clinicians will not act on an EEG classification they cannot interrogate, and a diagnostic report that can be silently edited after issue is a medico-legal liability.

Proposed Solution

Automate model selection so accuracy is not left to guesswork, attach SHAP and LIME explanations to every prediction, and hash each issued report onto a permissioned ledger so tampering is detectable.

Key Features

  • Multi-Model EEG Classification

    Automatic best-model selection across several ML architectures.

  • Explainable AI (SHAP, LIME)

    Makes predictions clear and clinically trustworthy.

  • Blockchain-Secured Reports

    Hyperledger-based SHA-256 hashing detects report tampering.

  • PDF Report Generation

    Detailed reports with room to expand to depression, anxiety, and OCD.

Technologies Used

Machine Learning

  • Scikit-Learn
  • Model selection
  • Signal features
  • Python

Explainability

  • SHAP
  • LIME
  • Matplotlib

Blockchain

  • Hyperledger
  • SHA-256 hashing
  • Integrity verification

Application

  • Flask
  • ReportLab
  • PostgreSQL

Project Workflow

  1. 1

    Signal upload

    EEG recordings are ingested and preprocessed.

  2. 2

    Model selection

    Several architectures compete; the best is chosen.

  3. 3

    Classification

    The selected model produces a prediction.

  4. 4

    Explanation

    SHAP and LIME expose the deciding features.

  5. 5

    Secured reporting

    The PDF report is hashed onto the ledger.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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