Share this project

Done

Share this project

Done
whee'll:Cheap Brain-Computer Interface (BCI) for Wheelchairs project video thumbnail
Replay with sound
Play with
sound

Motivated biomedical engineers with 3D printed EEG cap, Arduino ADC and classification algorithms. Helping reduced mobility patients.

Motivated biomedical engineers with 3D printed EEG cap, Arduino ADC and classification algorithms. Helping reduced mobility patients. Read More
22
backers
€870
pledged of €12,500 goal
0
seconds to go

Funding Unsuccessful

This project's funding goal was not reached on December 8, 2015.

Albert Martí
Project by

Albert Martí

First created  |  0 backed

Full bio Contact

About this project

Have you ever wondered how many people are not free to move by their own depend on someone? Have you ever wondered how much frustration these people have because of sitting on a wheelchair?

 
It is estimated that the total amount of people either with reduced mobility or taking care of people with these disability is larger than 100 Million.
It is estimated that the total amount of people either with reduced mobility or taking care of people with these disability is larger than 100 Million.

We aim to provide these people a simple solution that allows them to move freely without needeng another person or even without needing their arms. 

The idea is pretty simple: it consists of developing a cost-effective wheelchair controlled by the brain.

As you may know, our neurons in the brain work by firing small electrical currents, allowing smart communication between themselves. Since we are able to capture these small currents (known as EEG), it is possible to record a huge amount of information of the brain.

To do so, we have designed and 3D printed an EEG cap that allows to place a couple of electrodes and record these signals. 

This is our first prototype of EEG Cap to hold all electrodes and record brain signals
This is our first prototype of EEG Cap to hold all electrodes and record brain signals

Once all the electrodes are placed in the regions corresponding to the motor cortex, we need to convert the voltage difference measured to a digital signal that a computer can process. 

Our target signals are generated in the motor cortex, a brain region whose main function is to send an electrical estimulus in order to move the desired limb. The regions were we place our electrodes are labeled as C3,C4 and CZ
Our target signals are generated in the motor cortex, a brain region whose main function is to send an electrical estimulus in order to move the desired limb. The regions were we place our electrodes are labeled as C3,C4 and CZ

Most of the devices used to acquire these signals and send them to a computer are developed by enterprises with commercial purposes. This does not only makes the price increase, but also reduces the compatibility with other softwares, because only a few of them are open-source.

Since one of the main goals is to develop a cost-effective open source system, we got rid of commercial EEG devices and we are developing our own acquisition system. 

In order to provide a simple but replicable solution, we decided to build an Arduino-Based EEG system, which provides two main qualities to our project: on one hand Arduino is the most used and spread electronic board, and on the other hand it is relatively easy to code (which enables other people to build their own systems based on ours).

A schematic of the overall electronic circuit.
A schematic of the overall electronic circuit.

Once we have acquired EEG signals we need to process them in order to extract useful information corresponding to the desired direction of movement. Several nonlinear signal processing algorithms can be used so as to extract the most useful information. Their complexity and performance is different one from the other, which provides a high degree of freedom to chose the best balance between accuracy and optimization.

One of the simplest methods to identify motor cortex signals consist of decomposing the electroencefalogram according to different frequencies.
One of the simplest methods to identify motor cortex signals consist of decomposing the electroencefalogram according to different frequencies.

One of the most challenging aspects of the whole framework is to process acquired data in real-time and continue acquiring new signals simultaneously. To do so we are implementing specific and reliable algorithms combined with a pre-defined model obtained with ground-truth labeled data. We use advanced classification (machine learning) algorithms learned from the best teachers in the world.

The whole framework requires several softwares in order to process the data, create the classifier, identify relevant features and control the actuator
The whole framework requires several softwares in order to process the data, create the classifier, identify relevant features and control the actuator

We'll be able to read patients' thoughts and control a motor with this information. Join our ambitious journey!

This project involves not only a single person but a team of biomedical engineering undergraduates with strong background and broad knowledge about these combined fields.

Risks and challenges

The main threat regarding the final execution of the project remains on the challenge of being relly useful for disabled patients. In fact, the complexity of the technology is not extremely complicated, which lowers considerably the risk of not achieving our goal. We can't find any threats considering rewards, since everything in this project is cheap and open-source!

Learn about accountability on Kickstarter

FAQ

Have a question? If the info above doesn't help, you can ask the project creator directly.

Ask a question

Support this project

  1. Select this reward

    Pledge €1 or more About $1.07 USD

    Thank you very much for helping us provide an option to disabled patients who might not have free mobility and independence.

    Less
    Estimated delivery
    2 backers
  2. Select this reward

    Pledge €5 or more About $5 USD

    Whatsapp voice note from the team thanking you for helping us provide an option to disabled patients who might not have free mobility and independence.

    Less
    Estimated delivery
    2 backers
  3. Select this reward

    Pledge €10 or more About $11 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    Song from a member of the team.

    Less
    Estimated delivery
    5 backers
  4. Select this reward

    Pledge €20 or more About $21 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    Ready to 3D print model of the EEG Cap (.stl)

    Less
    Estimated delivery
    2 backers
  5. Select this reward

    Pledge €30 or more About $32 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    3D Model of the EEG Cap

    Sample recordings from both EEG and BCI

    Less
    Estimated delivery
    1 backer
  6. Select this reward

    Pledge €50 or more About $54 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    Customized 3D Model of the EEG Cap (name, logo, quote...)

    Labeled phantom data from EEG.

    Skype conversation with the team.

    Less
    Estimated delivery
    4 backers
  7. Select this reward

    Pledge €100 or more About $107 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    Customized 3D Model of the EEG Cap (name, logo, quote...) + List of compounds and instructions to make your own EEG acquisition system.

    Skype Conversation with the team.

    Less
    Estimated delivery
    4 backers
  8. Select this reward

    Pledge €200 or more About $215 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    Customized 3D Model of the EEG Cap (name, logo, quote...) + Instructions to make the WHOLE BCI and signal processing.

    Acknowledgement and special mention for you or your enterprise during the pitch of the project.

    Less
    Estimated delivery
    0 backers
  9. Select this reward

    Pledge €350 or more About $376 USD

    Whatsapp voice note from the team thanking for for helping us provide an option to disabled patients who might not have free mobility and independence.

    Customized 3D Model of the EEG Cap (name, logo, quote...) + Instructions to make the WHOLE BCI and signal processing. Access to full processing computer codes (Matlab).

    Acknowledgement and special mention for you or your enterprise during the pitch of the project.

    Less
    Estimated delivery
    0 backers

Funding period

- (29 days)