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BIONIX: A Wireless, Low-Cost Prosthetic Arm with Dual-Signal EEG and EMG Control

Pranesh Sathish Kumar

TL;DR

This work tackles the challenge of providing affordable, intuitive upper-limb prosthetics by developing a dual-modal EEG–EMG control system on low-cost hardware. It combines a consumer EEG headset (MindWave) with a MyoWare EMG setup, processed on two ESP32s to drive hand and elbow servos via wireless channels, demonstrating real-time, multi-DoF control. The study delivers a functional prototype, detailed methodology for reproducibility, and evaluates blink-based hand control alongside threshold-based elbow movement, achieving functional but modest EEG performance and practical EMG latency, with an overall end-to-end latency in the hundreds of milliseconds. The findings highlight a viable, scalable pathway toward accessible neuro-muscular prostheses for underserved populations, while outlining concrete directions to improve accuracy, latency, and durability.

Abstract

Affordable upper-limb prostheses often lack intuitive control systems, limiting functionality and accessibility for amputees in low-resource settings. This project presents a low-cost, dual-mode neuro-muscular control system integrating electroencephalography (EEG) and electromyography (EMG) to enable real-time, multi-degree-of-freedom control of a prosthetic arm. EEG signals are acquired using the NeuroSky MindWave Mobile 2 and transmitted via ThinkGear Bluetooth packets to an ESP32 microcontroller running a lightweight classification model. The model was trained on 1500 seconds of recorded EEG data using a 6-frame sliding window with low-pass filtering, excluding poor-signal samples and using a 70/20/10 training--validation--test split. The classifier detects strong blink events, which toggle the hand between open and closed states. EMG signals are acquired using a MyoWare 2.0 sensor and SparkFun wireless shield and transmitted to a second ESP32, which performs threshold-based detection. Three activation bands (rest: 0--T1; extension: T1--T2; contraction: greater than T2) enable intuitive elbow control, with movement triggered only after eight consecutive frames in a movement class to improve stability. The EEG-controlled ESP32 actuates four finger servos, while the EMG-controlled ESP32 drives two elbow servos. A functional prototype was constructed using low-cost materials (total cost approximately 240 dollars), with most expense attributed to the commercial EEG headset. Future work includes transitioning to a 3D-printed chassis, integrating auto-regressive models to reduce EMG latency, and upgrading servo torque for improved load capacity and grip strength. This system demonstrates a feasible pathway to low-cost, biologically intuitive prosthetic control suitable for underserved and global health applications.

BIONIX: A Wireless, Low-Cost Prosthetic Arm with Dual-Signal EEG and EMG Control

TL;DR

This work tackles the challenge of providing affordable, intuitive upper-limb prosthetics by developing a dual-modal EEG–EMG control system on low-cost hardware. It combines a consumer EEG headset (MindWave) with a MyoWare EMG setup, processed on two ESP32s to drive hand and elbow servos via wireless channels, demonstrating real-time, multi-DoF control. The study delivers a functional prototype, detailed methodology for reproducibility, and evaluates blink-based hand control alongside threshold-based elbow movement, achieving functional but modest EEG performance and practical EMG latency, with an overall end-to-end latency in the hundreds of milliseconds. The findings highlight a viable, scalable pathway toward accessible neuro-muscular prostheses for underserved populations, while outlining concrete directions to improve accuracy, latency, and durability.

Abstract

Affordable upper-limb prostheses often lack intuitive control systems, limiting functionality and accessibility for amputees in low-resource settings. This project presents a low-cost, dual-mode neuro-muscular control system integrating electroencephalography (EEG) and electromyography (EMG) to enable real-time, multi-degree-of-freedom control of a prosthetic arm. EEG signals are acquired using the NeuroSky MindWave Mobile 2 and transmitted via ThinkGear Bluetooth packets to an ESP32 microcontroller running a lightweight classification model. The model was trained on 1500 seconds of recorded EEG data using a 6-frame sliding window with low-pass filtering, excluding poor-signal samples and using a 70/20/10 training--validation--test split. The classifier detects strong blink events, which toggle the hand between open and closed states. EMG signals are acquired using a MyoWare 2.0 sensor and SparkFun wireless shield and transmitted to a second ESP32, which performs threshold-based detection. Three activation bands (rest: 0--T1; extension: T1--T2; contraction: greater than T2) enable intuitive elbow control, with movement triggered only after eight consecutive frames in a movement class to improve stability. The EEG-controlled ESP32 actuates four finger servos, while the EMG-controlled ESP32 drives two elbow servos. A functional prototype was constructed using low-cost materials (total cost approximately 240 dollars), with most expense attributed to the commercial EEG headset. Future work includes transitioning to a 3D-printed chassis, integrating auto-regressive models to reduce EMG latency, and upgrading servo torque for improved load capacity and grip strength. This system demonstrates a feasible pathway to low-cost, biologically intuitive prosthetic control suitable for underserved and global health applications.

Paper Structure

This paper contains 53 sections, 10 equations, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. EMG-controlled Prostheses: Strengths and Limitations
  3. EEG-controlled Prostheses and Brain–Computer Interfaces
  4. Hybrid EEG–EMG Control: Potential and Research Attempts
  5. Gaps in the Literature and Motivation for This Work
  6. Methodology
  7. Overview
  8. Hardware and Mechanical Design
  9. Parts and components
  10. Mechanical layout and assembly
  11. Biosignal acquisition
  12. EEG acquisition (MindWave Mobile 2 & ThinkGear protocol)
  13. EMG acquisition (MyoWare 2.0 & SparkFun Wireless Shield)
  14. Signal processing and preprocessing
  15. Quality control and poor-signal removal
  16. ...and 38 more sections

Figures (8)

  • Figure 1: Dual-modal EEG–EMG prosthetic arm system overview. EEG and EMG signals are acquired wirelessly by ESP32s, processed locally, and used to actuate prosthetic servos. Dashed arrows indicate wireless sensor-to-ESP32 communication.
  • Figure 2: EEG blink classifier training loss over epochs.
  • Figure 3: EEG blink classifier accuracy over epochs for training and validation sets.
  • Figure 4: Example of processed EEG signal over time with blink-related peaks.
  • Figure 5: Optimal placement of the MindWave EEG headset on the participant's forehead.
  • ...and 3 more figures