Brain Computer Interface Control of a Virtual Robotic System based on SSVEP and EEG Signal

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By:

Fatemeh Akrami

Supervisor:

Dr. Hamid D. Taghirad

October 2017

Brain Computer Interface Control of a Virtual Robotic

System based on SSVEP and EEG Signal

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Contents

Introduction

Material and Methods

System Integration

Experiment results

Conclusion

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• A direct connection pathway between the brain and external world.

• BCI acquires brain signal in response to a certain type of behavior and

decode it into the device control commands.

Introduction

System Integration Experiment

results Conclusion

Brain Computer Interface (BCI)

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• Response to flickering stimulus.

• Generates in the visual cortex of the brain.

• It contains stimulus frequency and its harmonics.

Introduction

results

Steady State Visual Evoked Potential

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Introduction

results Conclusion

PSD of SSVEP Signal

Amplitude spectrum of the SSVEP BCI signal for P7, O1, O2, P8 electrodes in 17Hz stimulus frequency

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• SSVEP needs less training time

• Information transfer rate is high

• It has higher classification accuracy

Introduction

results

Why SSVEP signal?

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Introduction

results Conclusion

SSVEP based BCI system

Typical structure of the SSVEP based BCI system

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Brain Computer Interface (BCI)

results

Visual stimulus

Introduction

Visual Stimulus Design

Mechanical design MCU and control color and frequency Connection between

MCU and PC

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results Conclusion

Visual stimulus

Introduction

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results

Data acquisition

Introduction _•

14 channels: AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4

• References: In the CMS/DRL noise cancellation

• Sampling rate: 128 SPS

• Resolution: 14 bits 1 LSB = 0.51μV

• Bandwidth: 0.2 – 43Hz

• Wireless and rechargeable

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Brain Computer Interface (BCI)

results Conclusion

Data acquisition

Introduction

• Distance between the stimulus and subject is 80 [cm].

• Visual stimulus frequencies are 11, 13, 15 and 17 [Hz].

• The color of the stimulus is set to green.

• Electrodes placed on O1, O2, P7 and P8 locations.

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Brain Computer Interface (BCI)

results

Data Processing

Introduction

M = mean value

C = Covariance matrix

The reference signal

Likelihood Ratio Test (LRT) method

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results Conclusion

Data Processing

Introduction

Likelihood ratio for static signal

The measure of association

L=1: data are perfectly correlated L=0: data are uncorrelated

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results

Virtual robotic arm

Introduction

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results Conclusion

Virtual robotic arm

Introduction

RV2AJ Simulink model

• The robot has two degree of freedom.

• Theta1 and Theta2 are inputs to determine the robot movement.

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results

System Integration

Introduction

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results Conclusion

State of the robot

Introduction

State of the robot corresponding to stimulus frequency

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results

Offline results

Introduction

• Data was recorded in ARAS lab

• Four subject participated in experiments

• Length of the data is 20 second in four different trails

• The experiment was performed in dim room

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results

Conclusion

Offline results

Introduction

• Each time window for processing is four second

• Accuracy is the number of the correct segments to the total

number of segments

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results

Online results

Introduction

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results

Conclusion

Online results

Introduction

Online result of LRT algorithm and Classiﬁcation output in test2

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results

Conclusion

Introduction • An effective strategy is done for implementation of complete BCI system.

• LRT method is evaluated in offline analysis and implemented for online detection.

• It is improved that the online detection with high accuracy in short time windows are possible.

• It is improved that the input stimulus frequencies are perfectly distinguishable.

• The experiments show promising features of the developed system for further application.

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