This is the complete step by step guide of Brain wave decoder project.We will cover each and every step with details and practical results.There are three main parts of the project The hardware,circuit designing and the software.In the hardware section we will discuss about EEG project applications,The selection of electrodes for capturing EEG waves. The circuit designing part consists of designing filters for extracting EEG waves.The last part is about test results and real time EEG wave simulations. The brain wave controlling EEG project consists of many sub sections.The complete project will be covered in the following posts.

Test Results

All the stages of the circuit have been working. The final output is low noise and amplified. Our aim was to get the beta wave because beta wave comes when we are alert and awake. The final circuit responds perfectly and accurately.

The signal is readable by the Arduino and reacts on eye and arm movement. Whenever an individual moves his eyes to the left the positive amplitude has been changed and similarly when moving eyes to the left the amplitude changes in the negative.

Here is the final output that is observed on the Oscilloscope.

The following figure shows the output wave form that is quite similar with the beta wave

The circuit responds on the eye blinks and eyes movement. As we can see the changes in the waveform when we move our eyes left or right. These changes have been observed in real time using MATLAB. There are continues changes in the ADC values of Arduino board. We can utilize the variation in the ADC values to control any application.

Finalizing the Project:

The whole EEG circuit has been placed in a box with the output indication LEDs. There are three LEDs Red, Yellow and Green. When we move eyes left and right the indication through two LEDs will show. The yellow remains in the normal state that indicates no change.

We have placed the EEG gold plated dry electrodes in the wearable cap to make the system portable and wearable.

As we can see that the box has three indicating LEDs with an LCD to indicates the message. When we move eyes left the corresponding Led turns on with the message on the LCD. We can make any of application by using the response of our EEG circuit. This is the simple medical prototype to show the response of the circuit.

Safety and Precautions

Electrically powered.
Remove all AC line connections
Proper skin preparation for proper contact
No combustible pollution.
Protecting the project from being mishandled by any user.
Proper wiring

Conclusion

The project object was to get the low power low noise brain signal that can be used to control real world application. In this project we learned about the technique to design and implement the low power low noise and high sensitive EEG portable system.

This project holds importance in almost every field of study especially in the field of medical and communication and control.We studied about the designing and behaviors of different electronic components in detail. That is the most important part of our filed.We studied wired and wireless protocol which is an interesting technology used in electronic market today.

Finally we have a Low power, Low noise and high sensitive portable brain wave decoding system that can be used for any real world applications.

Future recommendation

There are endless directions and applications of this project. As EEG system are widely used in medical application. The more robust system can be more helpful in medical application. As the system is wearable for long term so it can be used in any sort of medical and control applications. The EEG system development and sending brain signals wirelessly opens the new doors for various applications. In future it can be used as a communication device for paralyzed and disabled people. It can also very helpful for patient’s state monitoring. We can control quadcopter using brain waves or a wheelchair or a driverless car. The system is specially developed for medical applications. There are variety of possibilities for future application with brain wave decoder system.

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