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BCI Control for Wheelchair Movement using Mindwave Mobile

BCI Control for Wheelchair Movement

Abstract

A Brain computer interface (BCI) is a system that allows direct communication between a computer and a human brain, bypassing the body’s normal neuromuscular pathways. Instead of depending on peripheral nerves and muscles, a BCI directly measures brain activity associated with the user’s intent and translates the recorded brain activity into corresponding control signals for certain applications. The signals recorded by the system are processed and classified to recognize the intent of the user. Though the main application for BCIs is in rehabilitation of disabled patients, they are increasingly being used in other application scenarios as well. One such application is the control of wheelchair movement.

Independent mobility is core to being able to perform activities of daily living by oneself. Millions of people around the world suffer from mobility impairments and hundreds of thousands of them rely upon powered wheelchairs to get on with their activities of daily living. However, many patients are not prescribed powered wheelchairs at all, either because they are physically unable to control the chair using a conventional interface, or because they are deemed incapable of driving safely. For some of these people, non–invasive brain–computer interfaces (BCIs) offer a promising solution to this interaction problem.

In this project we have developed a cost effective BCI application that will help the physically challenged to lead an independent life with the help of their brain signals using non-invasive techniques.

Demonstration Video

What is Electroencephalography

Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. EEG is most often used to diagnose epilepsy, which causes obvious abnormalities in EEG readings. It is also used to diagnose sleep disorders, coma, encephalopathies, and brain death. EEG used to be a first-line method of diagnosis for tumors, stroke and other focal brain disorders. For the extraction of EEG signals, the electrodes are placed on the scalp as per the International 10-20 system.



 

Electrode placement on the scalp according to 10-20 system

 

What is BCI?

A brain–computer interface (BCI), often called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device.

Non-invasive BCIs

Most non-invasive BCI systems use electroencephalogram (EEG) signals; i.e., the electrical brain activity recorded from electrodes placed on the scalp. Non-invasive BCIs can be classified into 2 types, namely, evoked BCI and spontaneous BCI:

An evoked BCI exploits a strong characteristic of the EEG, the so-called evoked potential, which reflects the immediate automatic responses of the brain to some external stimuli.

Spontaneous BCIs are based on the analysis of EEG phenomena associated with various aspects of brain function related to mental tasks carried out by the subject at his/her own will.

Scope of BCI:

Amyotrophic lateral sclerosis (ALS) is a progressive disease that affects motor neurons, which are specialized nerve cells that are important for controlling muscle movement and strength. These nerve cells are found in the spinal cord and the brain. In ALS, motor neurons die over time, leading to problems with muscle control and movement. However no significant damage is done to sensory neurons.BCI uses these signals transmitted by the sensory neurons to control external devices.

About the Project:

In this project we aim to develop a BCI application that will help the physically challenged (Amoetyo lateral sclerosis patients) to lead an independent life with the help of their brain signals.

Block diagram:





Brain Signal Input:

The brain signals used here are Spontaneous EEG signals. These signals are associated with various aspects of brain function related to mental tasks carried out by the subject at his/her own will. The mental tasks include attention, eye blinks and eye movement for forward, reverse and stop actions respectively.

EEG Signal Acquisition Unit:

In this application a headgear is used for signal acquisition instead of the electrode cap. The headgear or the brainwave starter kit makes use of dry sensors which does not require application of a conductive gel between the sensors and the scalp. Also this device is much lighter and convenient for usage when compared to the conventional EEG sensors as it requires only one electrode for sensing. Another advantage of using this kit is that the data or brain signals are transmitted to the signal processing unit via Bluetooth connection which was not possible with the conventional signal acquisition methods.



 

Brainwave starter kit

 

Signal Processing Unit:

The signal processing unit used in this application is a laptop/PC. The brain signals are transmitted from the headgear via Bluetooth to MATLAB platform in the laptop. The digitized value is then passed on to suitable microcontroller through USB port for further mapping of brain signal values to control signals of the motors.

Current Booster:

An H bridge is an electronic circuit that enables a voltage to be applied across a load in either direction. These circuits are often used in robotics and other applications to allow DC motors to run forward or backwards. H bridges are available as integrated circuits. They can be built using discrete components.

Wheelchair prototype:

Two motors of 60rpm each are used to form a wheelchair prototype. The frame is constructed using aluminium sheets. The control signals from the H-bridge circuit are sent to the motors. Depending on the action performed, the control signals will cause the motor to run in either in clockwise, anticlockwise direction or stop.





Project Flowchart





Initially Bluetooth connection is established between the headset and the signal processing unit (PC/Laptop). Once the headset is turned on, depending on the requirements of the motor movements, actions are performed. The brain signals are now extracted using Brainwave kit and converted to digital values and transmitted to signal processing unit via Bluetooth. These values are then processed in MATLAB and mapped into control signals of required amplitude using Arduino (ATMega8) and are then used to activate the motors of the wheelchair prototype.

Conclusion:

When different subjects performed the same actions, the signal values obtained were within the same range but the delay varied from person to person. It was found that, this delay period could be reduced by training the headgear for that person.

FUTURE SCOPE

1. Control for wheelchair movement in 2D:

More number of sensors can be used for acquiring brain signals from different portion of the brain. These signals can then be processed in the similar manner and mapped to the control signals to obtain wheelchair movement in the other dimension (left, right etc.).

2. Home automation using BCI:

The processed brain signals can be used as control signals for different home appliances. Eg: turning on lights, emergency call etc.

3. BCI Speller:

In the above application, the microcontroller can be replaced by a speech IC. Whenever a particular action is performed, the corresponding brain signal can be mapped to a message which is spelt.

RESULTS AND CONCLUSIONS

Brain signals acquired for three different actions using Headgear were processed and then mapped to the control signals to obtain different movements of the wheelchair.

Forward:

1) The motors could be moved forward by focusing at a point or on a single thought. Brain signal values obtained during this action reached a peak of “100”, which was mapped to the control signals for clockwise movement of the motors using the Atmega8 board.



 

Values obtained during Forward motion

 

2)Reverse:

The motors could be moved backwards by continuously blinking the eyes. Two consecutive brain signal values below “10” were obtained during this action, which were mapped to the control signals for anticlockwise movement of the motors using the Atmega8 board.



 

Values obtained during Reverse motion

 

3)stop:

The motors could be stopped by the up eye movement. Five constant brain signal values were obtained during this action, which were mapped to the control signals for anticlockwise movement of the motors using the Atmega8 board.



 

Values obtained for Stop

 

When different subjects performed the same actions, the signal values obtained were within the same range but the delay varied from person to person. It was found that, this delay period could be reduced by training the headgear for that person.

Project Advantages:

  • Non-invasive signal acquisition.
  • Wireless transmission via Bluetooth.
  • Cost effective and portable.
  • Mapping of brain signals to corresponding motor movements.

Project Limitations

  • This BCI project is applicable only for 1D motion.
  • The brainwave starter kit needs to be trained initially to reduce the amount of delay considerably.

Mindwave Mobile related link

  • BCI Control for Wheelchair Movement using Mindwave Mobile demo video