Mansha

Abstract

“Mansha” is an advanced Brain-Computer Interface (BCI) system that enables direct communication between the brain and external devices using EEG technology, signal processing, and machine learning. Aimed at enhancing accessibility and independence, especially for individuals with disabilities, it offers intuitive, hands-free control by translating neural signals into actionable commands. The project prioritizes signal accuracy, processing speed, and adaptability, with applications ranging from assistive technologies and smart home automation to robotic control and neurorehabilitation. By addressing key challenges in BCI—such as signal variability and real-time responsiveness—Mansha contributes to a more inclusive, intelligent future in neurotechnology and AI.

Introduction

1. Introduction & Purpose

• BCI technology creates direct communication between the human brain and external devices, bypassing traditional input methods like touch or voice.

• Project Mansha focuses on building an EEG-based BCI system using signal processing and machine learning to enable hands-free control for users, especially individuals with physical disabilities.

• Applications include assistive devices, smart homes, robotics, and healthcare, promoting accessibility and independence.

2. Literature Survey

• Early BCI research centered on binary tasks using non-invasive EEG for communication (e.g., SCPs and ERPs).

• With AI and signal processing, modern BCI systems use methods like:

  • Common Spatial Patterns (CSP)

  • Wavelet Transforms

  • Power Spectral Density (PSD)

• Classifiers like SVM, k-NN, and CNNs have improved accuracy and response time.

• Applications have expanded into VR, IoT, and industrial automation.

• Clinical BCIs like BrainGate demonstrate potential in neurorehabilitation but face challenges like signal variability, noise, and hardware standardization.

3. Problem Statement

• Traditional interfaces (touch, voice) are inaccessible to users with motor impairments.

• Limitations of current BCI systems include:

  • Low signal fidelity

  • Noise interference

  • Long calibration times

  • Limited adaptability

• Mansha’s goal is to create a highly accurate, real-time, user-friendly BCI for diverse real-world applications.

4. Methodology

NeuroAgile Approach

• Combines Agile software development with hardware prototyping.

• Iterative design enables:

  • Continuous user feedback

  • Real-time validation

  • Adaptive model tuning

• Strong focus on user-centric design, especially for users with disabilities.

Development Process

1. Identify user needs

2. Select affordable EEG hardware (e.g., BioAmp EXG Pill, Arduino)

3. Train models on EEG signals

4. Integrate with IoT devices (using Bluetooth/Wi-Fi)

5. Test and optimize based on real-world feedback

5. System Design and Stakeholder Engagement

• Emphasizes real-time performance, modularity, and cross-platform compatibility.

• Designed to work with multiple communication protocols like MQTT, BLE, HTTP.

• Stakeholder collaboration ensures system relevance and practical impact.

6. Hardware Components

• EEG Headband: Captures brainwave activity.

• Arduino Uno: Converts EEG analog signals to digital.

• BioAmp Cable & Jumper Wires: Connect components.

• Electrode Gel & Gel Electrodes: Enhance signal quality and allow flexible placement.

7. Key Advantages of Mansha

• Hands-free interaction for users with limited mobility.

• Real-time responsiveness and machine learning adaptability.

• Scalable and modular architecture suitable for:

  • Assistive tech

  • Smart environments

  • Healthcare

  • Aerospace and defense

Conclusion

Project Mansha represents a major step forward in neuroadaptive interface design. By combining EEG signal acquisition, machine learning, and IoT integration, it delivers a robust, responsive, and inclusive BCI system with broad applications. It addresses key challenges of current BCI systems and promotes accessibility, autonomy, and next-gen human-computer interaction.

References

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Copyright

Copyright © 2025 Jitesh Kumar Sahu, Pranjal Mahapatra, K Yashwant Rao, Sparsh Verma, Eshank Sharma, Ms. Sonia Wadhwa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.