BMW: Brainwave Manipulated Wagon
Zijian Chen, Tiffany Jao, Man Qin, Xueling Zhao
Faculty Advisor: Prof. Qiangfei Xia
Abstract
System Overview
BMW (Brainwave Manipulated Wagon) is a robotic car that
can be remotely controlled by user’s brain EEG
(Electroencephalography) signals. Our system uses BCI
(Brainwave Computer Interface) to provide the
communication between our brain, the computer application
and the robotic car. The system uses a commercial EEG
headset to acquire EEG data. Then, the data is processed and
classified into command in the computer application. The
application transmits the command signal to the robotic car,
which will achieve desired operation. User is able to control
the car to move forward, backward, stop and turn left and
right.
Our system primarily utilizes two responses in the occipital
lobe region of the brain, which is more responsive to visual
perception.
I. Eye-Closed
i. Results in increases in alpha wave power
ii. Applies Bayesian classifier for the eye-closed
detection
II. SSVEP (Steady-State Visual Evoked Potentials)
i. Invokes by focusing on a light stimuli blinking at
fixed frequency
ii. Observes consecutive detection of dominant
frequency
Background
I. Eye-Open/ Eye-Closed
wireless 2.4GHz
(real time)
dominant frequency
SSVEP
Classifier
Eye-Open
EEG Voltage Data vs Time
4420
Voltage(uV)
4400
Voltage(uV)
4380
4360
4340
4320
4300
1
2
3
40
30
30
20
10
0
4 6 8 10121416
22
Database
(Training Data)
(training)
Alpha, beta power
command
4
5
283032
40
45
50
20
10
0
01
6 8 10 12 14 16
22
28 30 32
40
45
50
Frequency(Hz)
Figure(d)
II. SSVEP
Arduino
•
Bluetooth
HC05
Robotic Car
Control the car to turn left or right upon detection of
SSVEP signal.
Magnitude vs Frequency for 10Hz
Stimuli
Magnitude vs Frequency for 15Hz
Stimuli
1000
1000
800
800
Magnitude
User Action
Avg. Detection
Accuracy
Time (sec)
Magnitude
Specifications
600
400
200
600
400
200
0
0
0
Close eyes
Forward
if the car is currently stopped
Close eyes
if the car is current moving
forward/backward
Left Turn Stare at 10Hz light stimulus
Right Turn Stare at 15Hz light stimulus
Backward
3
Figure (a) and (b): Voltage EEG Data versus Time. Voltage EEG data is the voltage
measurement resulting from neuron activities. These two graphs do not show much
information about alpha and beta wave.
Figure (c) and (d): Now, the data is interpreted in frequency domain. A dominant spike is
observed within the alpha wave region under eye-closed condition, which causes the
total alpha wave power to increase significantly. At the same time, beta wave power
does not change much.
Emotiv EPOC
headset
Stop
2
Eye-Closed
FFT Magnitude vs Frequency
Frequency(Hz)
Figure(c)
User Interface
Command
1
Time(Sec)
Figure(b)
40
(training)Alpha, beta power
Bayesian
Classifier
5
Eye-Closed
Raw voltage EEG data vs Time
Eye-Open
FFT Magnitude vs Frequency
Computer
(real time)
Alpha, beta power
4
4420
4400
4380
4360
4340
4320
4300
Time(sec)
Figure(a)
0
C# Application
Signal
Processing
Control the car to move forward, backward, and stop by
detecting the rise of alpha wave power.
Magnitude
Block Diagram
EEG Voltage data
•
Magnitude
Brainwaves are produced by synchronized electrical pulses
from masses of neurons communicating with each other.
Types of Brainwaves:
– Delta
0-4Hz
– Theta
5-7Hz
– Alpha*
8-12Hz
– Beta
13-30Hz
– Gamma
30+Hz
*Occipital alpha waves during eye-closed periods are the
strongest EEG brain signals.
Close eye for a significant
longer time
5
10
15
20
25
Frequency(Hz)
Figure (e)
2.15
7.04
90.9%
86.7%
6.51
90.5%
6.01
77.3%
0
5
10
15
20
25
Frequency(Hz)
Figure (f)
Figure (e): A significant spike at 9.25Hz can be observed when staring at the 10Hz
stimuli
Figure (f): A significant spike at 12.75Hz is observed when staring at the 15 Hz stimuli
Acknowledgement
Specially thank Prof. Xia for being our advisor, providing us
feedback and sponsoring us to purchase the Emotiv EPOC
headset. Thank you to Prof. Soules, Prof. Tessier, Prof. Mettu
of Tulane University, Mr. Alexander de Geofroy, Prof. Rebecca
Spencer and the Cognac Lab. We also want to thank Fran
Caron and Terry Bernard for ordering the parts for our
project.
Department of Electrical and Computer Engineering
ECE 415/ECE 416 – SENIOR DESIGN PROJECT 2015
College of Engineering - University of Massachusetts Amherst
SDP15
User Interface
Eye-Closed Detection
The eye-closed detection is based on the Bayesian classifier.
1. Find Reference Point: Determines the threshold for high
alpha and high beta based on the training set data
2. Analysis Training Set: Updates the probability table to
calculate the classified result
3. Classification: Determines if the real-time alpha and beta
power are more likely to be eye-closed or eye-open
Training Interface
• Gives user the direction to perform the corresponding
training
• Gathers alpha and beta power during the eye-open and
eye-closed training
Sensor Contact Form
• Checks if the reference sensors and the O1 sensors are in
good contact
Forward/Stop/Backward
Close eyes until the car
reacts/detect alpha high
Car
Forward
Keep eyes closed after car
stops until car backward
Car
Backward
Car Stop
Close eyes until the car
stops/detect alpha high
Close eyes until the car
reacts/detect alpha high
Control Interface
• Display
– 10 Hz and 15 Hz light stimuli for invoking SSVEP
response
– The real-time command detection result
– Alpha and Beta Power vs Time graph
• Control
– Connection with the robotic car
– Start / Stop the command detection
SSVEP Detection
Results
1. Analysis Training Set: Determines the dominant frequency
when the user is looking at the 10 Hz and 15 Hz blinking
square
2. Classification: Checks for consecutive occurrence of
dominant frequency
Experimental Results:
– 9.25 Hz for left turn
– 12.75 Hz for right turn
Patten
Observation
Source of Error
EyeOpen/
EyeClosed
Alpha power increases
significantly compared to
beta power
SSVEP
Dominant Frequency
around:
1. Brightness of screen
- 9.25 Hz for starting at the 2. Accidentally glances at
10Hz stimuli
the wrong blinking
- 12.75 Hz for staring at the
square
15Hz stimuli
1. Muscle Movement
2. Tiredness
- also leads to increase
in alpha power
Robotic Car
Cost
Development
Production
Part
Price
Part
Price
Arduino Uno
24.51
Arduino Uno
19.96
Motor * 2
3.90
Motor *2
3.90
Bluetooth receiver
7.59
Bluetooth receiver 7.59
L298N Motor Driver 34.95
L298N Motor Driver 27.96
EPOC Headset
Total
EPOC Headset
Total
699
769.95
699
758.41
HC-05 Bluetooth module is used to receive data, which is a
digital signal from the computer. An Arduino board is used
to retrieve the number and convert it into four digital
output. Output pins are hooked to L298N Motor driver
board. L298N controls the two motors, which allows the car
to move.