Projects:2014S1-26 Brain Computer Interface Control for Biomedical Applications
Every year, many people around the world suffer from strokes and lose the ability to control parts of their bodies. Currently, high amounts of money, time and effort is put into the research of ways to allow stroke victims to regain control of their bodies. The purpose of this project is to facilitate the rehabilitation and mobility of people with this kind of disability/injury/condition. The aim of this project is to interpret EEG signals corresponding to lifting and dropping from an individual who has lost the use of their arms due to stroke. This is completed using an Emotiv EPOC headset to categorize these signals with the use of a brain computer interface(BCI) which sends them to an applicator that provides sensory feedback to the brain. This is a completely new project and does not expand on any previous projects. The final outcome is to achieve a brain computer interface to rehabilitate stroke patients, allowing them to regain independent movement of their arms. This project is a proof of concept, and future teams at the University of Adelaide will be able to expand on the already existing hardware and software this project will produce.
The rehabilitation of stroke victims' arms is a major development and allows them to lead a quality of life similar to before the stroke. Our Brain Computer Interface will assist stroke victims to regain control of their arms. In order to do this we utilise two key techniques, Electroencephalography and Neuroplasticity. Electroencephalography is used to measure the brain waves of the user and neuroplasticity will allow the user to redevelop brain function through forcing the brain to utilise new neural pathways. The two main areas of work for the project are the design and construction of an applicator and the creation of code on a PC to control an Arduino which will allow EEG signals to be identified and relayed to the applicator.
The electroencephalogram is a measure of brain waves. It is a test that provides evidence of how the brain functions over time and is used in the evaluation of brain disorders. This includes weakening of specific parts of the body such as weakness associated with a stroke. EEG signals are recorded by placing multiple electrodes on the scalp. In relation to the project the Emotiv EPOC headset is used to retrieve the EEG signals using 14 electrodes placed on the head and two reference electrodes. The headsets software development kit Emotiv Education Edition then allows the interpretation of these signals, so that they can be used in identifying signals as lifting or dropping.
Neuroplasticity refers to the potential that the brain has to reorganize by creating new neural pathways to adapt, as it needs. The role of neuroplasticity is widely recognized in healthy development, learning, memory, and recovery from brain damage. In relation to the project it is hoped that we can rehabilitate the neural pathways in stroke victims brains by allowing the user to focus thoughts towards a practical outcome. This is a commonly used rehabilitation technique for people who have suffered neurological problems such as a stroke.
There are two main deliverables present in the project. The applicator is the first deliverable. It is a piece of hardware that allows the user to rest their arm in. The headset then monitors the EEG signals the user is creating and the applicator lifts of drops accordingly. The second deliverable is software for the brain computer interface. The software allows for the interpretation of EEG signals from the user and determines whether they are lifting or dropping. This information is sent through to the applicator and it moves accordingly.
In addition to the primary deliverable which is a working brain computer interface, there are a number of University project milestones which also need to be considered.
- Proposal seminar
- Progress report
- Final seminar
- Final report
- User Manual
- Project exhibition
- Obtain Emotiv EPOC headset and SDK
- Obtain a control device to interface between PC and applicator
The high level plan gives a brief overview of the main stages of the project.
- Phase 1
- Headset Configuration/Data acquisition
- Research brain computer interfaces and how they can be implemented.
- Research EEG and Neuroplasticity.
- Phase 2
- Create a design for an applicator used to move the user's arm up and down.
- Create code in order to interpret, decipher and send the relevant data from the headset to the applicator to make it move.
- Phase 3
- Construction of the applicator.
- Software and hardware integration.
- Final changes to code to ensure requirements are met.
- Phase 4
- Testing to ensure the applicator is safe and functions correctly.
Work Breakdown Structure
In order to effectively manage the project and ensure the equal distribution of work, a work breakdown structure was created, which allowed the allocation of work as shown below.
The work was broken down into two sections. One section focused on the hardware and applicator. The other section focused on the creation of the software to interpret the input signals and control the applicator. Anthony was assigned the software, while Benjamin was assigned the hardware, ensuring both members shared the workload fairly.
- Research EEG and Neuroplasticity. Learn about how they work/how they can be integrated into the project to achieve desired outcomes.
- Research and find the best software to use to interpret EEG signals effectively and allow them to be communicated to other programs.
- Once software is chosen, discover how to interpret the EEG signals coming from the headset to relate to a user lifting or dropping their arm.
- Discover how the software communicates with other applications. The required communications are with the Arduino IDE and Microsoft Visual C++ 2010 Express.
- Create code in Visual C++ which will recognize if a lift signal or drop signal has come from the Emotiv SDK.
- Create code in Visual C++ which will send through information about which signal can through to the Arduino chip.
- Create code in Visual C++ which will implement a training mode in the Emotiv SDK. The user must train the SDK before it is able to accurately pick up which EEG signals are coming from the headset.
- Create code in Visual C++ which will implement a challenge mode allowing stroke victims to be prompted to do an exercise and then are able to check their score afterwards.
- Acquire Autodesk software for development of applicator schematics.
- Draw up basic designs of how the applicator will look.
- Gather information about the average length and weight of a humans arm. This will determine the length the rest will need to be and the material that will be required to hold the arm. This will also give information on how powerful a motor will have to be to lift and drop a human arm.
- Acquire a suitable motor.
- Acquire a suitable battery which can power this motor.
- Create applicator design in Autodesk to be given to the workshop.
- Construct applicator.
Products and Outcomes
This section details what was completed in the project and how it will be used in the future.
- Applicator has been created which assists in moving the stroke victims arm up or down.
- BCI program has been created to interface between the Emotiv EPOC headset and the applicator to move the arm when the appropriate thought is detected.
- Aims to assist in the rehabilitation of stroke victims by providing neural feedback which helps stimulate neuroplasticity.
- The project will be able to be improved and modified for similar applications by final year students in the future.
This section lists all tasks that have been completed during the lifespan of the project.
- Signals are able to be trained and interpreted using the EPOC headset within the created programs console.
- Communication between the program and the Arduino has been established to power the motor in either direction once the relevant signal has been detected.
- Extra power is delivered to the motor via an external battery which is connected via the Arduino Motor Shield.
- Design of Applicator has been completed and has completed construction.
- User interfacing has been added to the program including Audio prompts.
- Challenge mode has been added to the program to give users a feedback score on their attempted movements.
- A windscreen wiper motor has been acquired to move the applicator.
- The Arudino UNO R3 has been acquired.
- A 12V rechargeable battery has been acquired to power the motor.
- The Arudino Motor Shield R3 has been acquired.
- Above parts have been combined to move the motor clockwise or anti-clockwise.
- User Manual has been completed.
- Mr Anthony Reveruzzi
- Mr Benjamin Traeger
- Associate Professor Mathias Baumert
- Mr David Bowler
- Emotiv EPOC Neuroheadset
- Standard PC or Laptop
- Microsoft Visual C++ 2010 Express
- Emotiv Education Edition SDK v184.108.40.206
- Arduino UNO R3
- Arduino UNO Motor Shield R3