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‎_projects/mcba.md‎

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@@ -4,6 +4,7 @@ title: Mind Controlled Bionic Arm with Sense of Touch
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description: Imagine a prosthetic arm that functions like your natural arm. You wear a headband, and with the thought process, the working signal from mind connects to the prosthetic about moving the arm, it responds accordingly—just like your real arm!
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tags: Bionic Arm Robotics Biotechnology Mind Control Prosthetics
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giscus_comments: true
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citation: true
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img: /assets/img/mcba_logo.jpeg
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date: 2024-12-12
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featured: true
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name: Lovely Professional University
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bibliography: papers.bib
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citation: true
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# Optionally, you can add a table of contents to your post.
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# NOTES:
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toc: true
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---
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# Abstract
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## Abstract
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Advancements in bionic technology are transforming the possibilities for restoring hand function in individuals with amputations or paralysis. This paper introduces a cost-effective bionic arm design that leverages mind-controlled functionality and integrates a sense of touch to replicate natural hand movements. The system utilizes a non-invasive EEG-based control mechanism, enabling users to operate the arm using brain signals processed into PWM commands for servo motor control of the bionic arm. Additionally, the design incorporates a touch sensor (tactile feedback) in the gripper, offering sensory feedback to enhance user safety and dexterity.
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The proposed bionic arm prioritizes three essential features:
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1. Integrated Sensory Feedback: Providing users with a tactile experience to mimic the sense of touch (signals directly going to the brain). This capability is crucial for safe object manipulation by arm and preventing injuries
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2. Mind-Control Potential: Harnessing EEG signals for seamless, thought-driven operation.
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3. Non-Invasive Nature: Ensuring user comfort by avoiding invasive surgical procedures.
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This novel approach aims to deliver an intuitive, natural, and efficient solution for restoring complex hand functions.
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---
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## Methodology
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### 1. Data Collection and Dataset Overview
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The model development utilized a publicly available EEG dataset comprising data from 60 volunteers performing 8 distinct activities [3]. The dataset includes a total of 8,680 four-second EEG recordings, collected using 16 dry electrodes configured according to the international 10-10 system [3].
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• Electrode Configuration: Monopolar configuration, where each electrode's potential was measured relative to neutral electrodes placed on both earlobes (ground references).
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• Signal Sampling: EEG signals were sampled at 125 Hz and preprocessed using:
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- A bandpass filter (5–50 Hz) to isolate relevant frequencies [3].
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- A notch filter (60 Hz) to remove powerline interference [3].
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### 2. Data Preprocessing
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The dataset, originally provided in CSV format, underwent a comprehensive preprocessing workflow:
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• The data was split into individual CSV files for each of the 16 channels, resulting in an increase from 74,441 files to 1,191,056 files.
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• Each individual channel's EEG data was converted into audio signals and saved in .wav format, allowing the brain signals to be audibly analyzed.
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• The entire preprocessing workflow was implemented in Python to ensure scalability and accuracy.
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The dataset captured brainwave signals corresponding to the following activities:
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1) BEO (Baseline with Eyes Open): One-time recording at the beginning of each run [3].
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2) CLH (Closing Left Hand): Five recordings per run [3].
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3) CRH (Closing Right Hand): Five recordings per run [3].
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4) DLF (Dorsal Flexion of Left Foot): Five recordings per run [3].
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5) PLF (Plantar Flexion of Left Foot): Five recordings per run [3].
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6) DRF (Dorsal Flexion of Right Foot): Five recordings per run [3].
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7) PRF (Plantar Flexion of Right Foot): Five recordings per run [3].
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8) Rest: Recorded between each task to capture the resting state [3] [4].
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### 3. Feature Extraction and Classification
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Feature extraction and activity classification were performed using transfer learning with YamNet [5], a deep neural network model.
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• Audio Representation: Audio files were imported into MATLAB using an Audio Datastore [6]. Mel-spectrograms, a time-frequency representation of the audio signals, were extracted using the yamnetPreprocess [7] function [8].
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• Dataset Split: The data was divided into training (70%), validation (20%), and testing (10%) sets.
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Transfer Learning with YamNet [5] [8]:
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- The pre-trained YamNet model (86 layers) was adapted for an 8-class classification task:
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-> The initial layers of YamNet [5] were frozen to retain previously learned representations [8].
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-> A new classification layer was added to the model [8].
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- Training details:
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-> Learning Rate: Initial rate of 3e-4, with an exponential learning rate decay schedule [8].
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-> Mini-Batch Size: 128 samples per batch.
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-> Validation: Performed every 651 iterations.
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### 4. Robotic Arm Design and Simulation
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A 3-Degree-of-Freedom (DOF) robotic arm was designed using MATLAB Simulink and Simscape toolboxes. To ensure robust validation:
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• A virtual environment was developed in Simulink, simulating the interactions between the trained AI models and the robotic arm.
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• The simulations served as a testbed to evaluate the system's performance before real-world integration.
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### 5. Project Progress and Future Directions
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Completed Tasks:
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1. AI Model Development: Successfully trained models to classify human activities based on EEG signals.
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2. Robotic Arm Design: Designed a functional 3-DOF robotic arm with simulated controls.
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3. Virtual Simulation: Validated AI-robotic arm interactions in a virtual environment.
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Future Directions:
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1. Hardware Integration: Implement the developed AI models into physical robotic hardware for real-world testing.
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2. Real-Time EEG Acquisition: Develop a system for real-time EEG data acquisition and activity classification.
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3. Tactile Feedback System: Integrate tactile sensors with the robotic arm for real-world sensory feedback, complemented by Simulink-based simulations.

‎assets/bibliography/papers.bib‎

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---
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---
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@misc{nprnews,
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url={https://www.npr.org/sections/health-shots/2021/05/20/998725924/a-sense-of-touch-boosts-speed-accuracy-of-mind-controlled-robotic-arm},
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journal={NPR},
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year={2021},
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month={May},
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title={Scientists Bring The Sense Of Touch To A Robotic Arm}
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}
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‌
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‌@misc{transferlearning_matlab,
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url={https://in.mathworks.com/help/audio/ug/transfer-learning-with-pretrained-audio-networks.html},
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journal={Mathworks.com},
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year={2024},
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title={Transfer Learning with Pretrained Audio Networks}
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}
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‌@misc{classify_sounds_using_yamnet_2021,
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url={https://in.mathworks.com/help/audio/ref/yamnetpreprocess.html},
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journal={Mathworks.com},
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year={2021},
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title={yamnetPreprocess}
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}
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‌@misc{audio_datastore,
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url={https://in.mathworks.com/help/audio/ref/audiodatastore.html},
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journal={Mathworks.com},
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year={2021},
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title={audioDatastore}
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}
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‌@misc{yamnet_github,
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url={https://github.com/tensorflow/models/tree/master/research/audioset/yamnet},
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journal={GitHub},
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year={2024},
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author={Google and Ellis, Dan and Plakal, Manoj},
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}
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@article{asanza_2023,
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title={MILimbEEG: An EEG Signals Dataset based on Upper and Lower Limb Task During the Execution of Motor and Motorimagery Tasks}, volume={2}, url={https://data.mendeley.com/datasets/x8psbz3f6x/2},
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DOI={https://doi.org/10.17632/x8psbz3f6x.2},
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journal={Mendeley Data},
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author={Asanza, Victor and Montoya, Daniel and Lorente-Leyva, Leandro Leonardo and Peluffo-Ordóñez, Diego Hernán and González, Kléber},
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year={2023},
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month={July}
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}
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‌@misc{https://doi.org/10.5524/100295,
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doi = {10.5524/100295},
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url = {http://gigadb.org/dataset/100295},
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author = {Cho, Hohyun and Ahn, Minkyu and Ahn, Sangtae and {Moonyoung Kwon} and Jun, Sung Chan},
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keywords = {ElectroEncephaloGraphy(EEG), Motor imagery, EEG, brain computer interface, performance variation, subject-to-subject transfer},
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language = {en},
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title = {Supporting data for "EEG datasets for motor imagery brain computer interface"},
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publisher = {GigaScience Database},
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year = {2017},
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copyright = {CC0 1.0 Universal}
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}

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