An EEG-Controlled Motion
System for Lower Limb
Exoskeleton
STRIDIX : Bringing hope through technology
Abdulrahman Hatem Abdo Aldois
Ahmed Fares Mohammed Al-Arashi
Aseel Abdulmughni Ali Ghalib
Abdullah Saleh Alsanbi
Abdalmola Yaqoob Ahmed Alselwi
TABLE OF CONTENTS
Background &
Introduction 01 04 Literature Review
Problem System
Statement 02 05 Architecture &
Control Flow
Hardware
Objectives 03 06 Components
TABLE OF CONTENTS
Mechanical
Design 07 09 Challenges Faced
Implementation
Results, Analysis
Assembly & 08 10 & Conclusion
Integration
11 Future work
01
INTRODUCTION
INTRODUCTION
What is the Paraplegia?
Paraplegia is the Loss of movement
and sensation in the lower body.
Usually caused by :
spinal cord injuries due to trauma
(accidents or falls) , medical conditions .
INTRODUCTION
Objective:
Develop an EEG-controlled exoskeleton for paralyzed patients to enhance
mobility.
Major components :
Brain signal detector motors Arduino Mega
0
2
Problem
Statement
02Problem Statement
The effect on quality of life
significantly impacts mobility and
independence, making daily activities
challenging for affected individuals.
Traditional rehabilitation methods require
constant manual support from therapists,
which can be exhausting and costly.
02 Problem Statement
• Enable Autonomous, User-Driven Rehabilitation
We Aim To • Empower patients to control their recovery process.
• Reduce reliance on caregivers during therapy.
• Low-Cost, Safe, and User-Customized Control
THE GOAL • Ensure accessibility for a wider range of patients.
• Allow personalization to fit individual user requirements.
03
Objectives
03 Objectives
General Objective
To design and implement a cost-effective, EEG-controlled
lower limb exoskeleton that enhances mobility, independence,
and rehabilitation for paraplegic patients by translating brain
signals into controlled motion.
03 Objectives
Specific Objectives
1. To develop a brain-computer interface using the MindWave EEG
headset to detect user attention levels and translate them into
control commands.
2. To build a mechanical exoskeleton structure using lightweight,
adjustable materials suitable for various user body sizes.
3. To implement real-time motor control using Arduino Mega and
PI controllers for smooth and responsive movement.
4. To integrate Bluetooth-based wireless communication
between the EEG headset and control system.
5. To ensure system safety through manual override, emergency
stop mechanisms, and user harnessing.
0
4
Background &
Literature
Review
04 Background & Literature Review
Types of Exoskeletons:
1 ) Passive : 2) Active :
Mechanical support without power; reduces strain Powered assistance; responds to user movements.
04Background & Literature Review
Types of Exoskeletons:
3 ) Hybird :
Combines passive and active features for versatility.
04 Background & Literature Review
Control Methods :
•EEG-based: Mind control
•EMG-based: Muscle activation
•Model-based: Predictive adjustment
•Hybrid control: EEG + EMG for better precision
Known Issues :
Limited joint freedom, heavy batteries, poor terrain adaptation, high cost
05
System
Architecture &
Control Flow
05 System Architecture & Control
Flow
• Block Diagram
05 System Architecture & Control
• Dual Mode System : Flow
EEG mode EEG mode
• Feedback Loop System : Encoder/Resistor feedback → Arduino
06
Hardware
Components
06Hardware Components
1 ) EEG Headset: MindWave Mobile 2 (with an Attention Level of 0–100 scale )
2) Bluetooth (HC-05) 3) Motor Drivers BTS7960 4) Adjustable Steel 201
5) Motors: High-torque DC motors (100–150 Nm) .
6) Power Supply: 2* 20V 2Ah Li-ion battery .
07
Mechanical
Design
07 Mechanical Design
• Steel 201: Chosen for strength-to-weight ratio
• Key Elements:
1) 30×30 mm and 25×25 mm square tubes
2) 32 mm pipe in rotation areas
3) Leg support straps, backplate, and safety harnesses
• Tensile & buckling strength calculations confirm 290–360 Kg load
capacity
• Adjustable for different patient sizes
08
Implementatio
n & Testing
08Implementation & Testing
• Mechanical structure assembled via welding and bolting
• Wiring of motors, drivers, power, and sensors
• MindWave paired to Arduino Mega via HC-05
• User secured via harness + adjustable telescopic fit
• Software loaded and tested with live attention signals
08Implementation & Testing
• Functional Testing: stand and sit , walk & Stop .
• Battery Performance
• Motor Efficiency: The PI controller helps maintain stable torque,
ensuring consistent performance without significant drops over
time.
• Control Response: A key function of the PI controller is to provide
fast and accurate response, which is crucial for dynamic
movements.
08Implementation & Testing
Electronics & Control Logic
• Arduino programmed via C++ using ThinkGear protocol
The MindWave Mobile 2 connects to the Arduino Mega via Bluetooth (HC-05). In this setup, the Arduino acts as
the master and the MindWave Mobile 2 operates as the slave. Once paired, the Arduino opens a virtual serial
port to read real-time data sent using the ThinkGear Communication Protocol (TGCP).
• Bluetooth communication with MindWave
• Attention Threshold: >60 triggers movement
• PI Controller:
- Smooth motor response
- Auto-tuning using step response
• Manual override remote included
• Emergency stop integrated
09
Challenges
Faced
09 Challenges Faced
• Bluetooth Disconnection: (MindWave Arduino)
• EEG Noise: During physical movement
• Variable Resistor Degradation: Used as encoder
substitute, affecting precision
• Variable Resistor Placement: Ensuring precise
rotation with the motor
• Mechanical Misalignment: In brackets (resolved via
re-machining)
• Tuning PI Controller: For different load conditions
10
Results,
Analysis &
Conclusion
10 Results, Analysis & Conclusion
• Exoskeleton effectively executed motion commands
from EEG signals
• Assisted users with sitting, standing, and walking
• Improved physical activation vs. passive devices
• Patients reported increased comfort and independence
• Compared favorably to wheelchairs and scooters in
physical rehabilitation value
10 Results, Analysis & Conclusion
• Successfully demonstrated EEG-based control for forward movement
and stopping using brain attention signals.
• Manual control method enabled additional actions: sit, get up, move
forward, and stop.
• System provided real-time, attention-triggered motion with Arduino +
PI controller integration.
• Improved user independence and rehabilitation potential with built-in
safety features (manual override & emergency stop).
11
Future work
11 Future Work
• Use multi-channel EEG for more complex
control
• Implement regenerative braking to self-charge
battery
• Upgrade feedback: Add visual/audio user
interface
• Replace frame with carbon fiber or other
composites for weight reduction
• Enhance signal processing with machine
learning
THANKS!
Do you have any questions?
Abdulrahman Hatem Abdo Aldois
Ahmed Fares Mohammed Al-Arashi
Aseel Abdulmughni Ali Ghalib
Abdullah Saleh Alsanbi
Abdalmola Yaqoob Ahmed Alselwi
