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MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System

Maha Nawaz, Abdul Basit, Muhammad Shafique

TL;DR

MindArm tackles the affordability and safety barriers of brain-controlled prosthetics by leveraging a non-invasive EEG interface and a Transformer-based decoder to drive a $4$-DoF prosthetic arm. The system runs on a low-cost OpenBCI Ganglion setup, uses UDP streaming for data transport, and a DNN trained on thousands of samples to translate brain signals into three distinct actions. Key contributions include noise-robust EEG processing, a high-performing Transformer model with validation accuracy near $97.1\%$, and a modular, 3D-printed prosthetic design with a total cost around $550$. Overall, MindArm demonstrates that accessible, non-invasive BCIs can achieve practical real-time control, broadening potential impact across diverse socioeconomic contexts.

Abstract

Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately $500-550, including $400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices.

MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Prosthetic Arm System

TL;DR

MindArm tackles the affordability and safety barriers of brain-controlled prosthetics by leveraging a non-invasive EEG interface and a Transformer-based decoder to drive a -DoF prosthetic arm. The system runs on a low-cost OpenBCI Ganglion setup, uses UDP streaming for data transport, and a DNN trained on thousands of samples to translate brain signals into three distinct actions. Key contributions include noise-robust EEG processing, a high-performing Transformer model with validation accuracy near , and a modular, 3D-printed prosthetic design with a total cost around . Overall, MindArm demonstrates that accessible, non-invasive BCIs can achieve practical real-time control, broadening potential impact across diverse socioeconomic contexts.

Abstract

Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately 400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices.
Paper Structure (12 sections, 22 figures, 4 tables, 1 algorithm)

This paper contains 12 sections, 22 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. State-of-the-Art Solutions and Their Limitations
  3. Scientific Challenges
  4. Our Novel Contributions
  5. Methodology
  6. EEG Data Collection & Extraction
  7. Neural Network Training
  8. Prosthetic Design and Label Feedback
  9. Results and Discussion
  10. Experimental Setup
  11. Analysis
  12. Conclusion

Figures (22)

  • Figure 1: Global Incidence of Traumatic Amputations. Based on the work by Yuan et al. 10.3389/fpubh.2023.1258853 published in Front. Public Health, this map illustrates the age-standardized amputation rates across 204 countries and territories. Areas with higher ASIRs could benefit significantly from the MindArm prosthetic solution, which offers a cost-effective and accessible alternative to assist individuals who have experienced limb loss, thus addressing the global need for affordable prosthetic care.
  • Figure 2: Our MindArm Workflow: EEG electrodes capture the brains' micro-voltage signals, which are then collected by the brain-computer interface (BCI) system with GUI. The data undergoes decomposition, cleaning, and temporary cloud storage before classification by the neural network. After training, the system predicts intended actions in real-time, and these actions are communicated to the prosthetic limb, enabling it to move accordingly.
  • Figure 3: MindArm Methodology: (a) Data Collection: EEG voltages are transmitted via Bluetooth to the GUI, which processes the data through a Fast Fourier Transform (FFT). The decomposed data are then saved to the cloud for further processing. (b) Neural Network Training: The dataset undergoes cleaning and is categorized into three groups. To accommodate UDP communication latency, the sampling rate is adjusted to 40 Hz. A window size of 80 samples, including a feature set of 20, is selected for training the network, which is subsequently saved to the cloud. (c) Deployment: The trained network interfaces with the prosthetic, designed with servo motors to enable 4 Degrees of Freedom (4DoF) movement. Network outputs are converted into servo actuations to perform the intended tasks.
  • Figure 4: The electrodes are attached to an adjustable band that has different slots for peripheral EEG readings.
  • Figure 5: 10-20 system for electrode placement, highlighting the region FP1, FP2, T3, and T4 being used for the measurements.
  • ...and 17 more figures