HCMC University of Technology and Education
Faculty of Mechanical Engineering
EEG Based BCI System
for Driver’s Arm Movements Identification
Author: E. Zero, C. Bersani and R. Sacile, University of
Genova, Italy
Instructor: Tran Ngoc Dam
Presenter: Ho Van Nhan
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Contents
1. Introduction
2. Method
2.1. Test Drive
2.2.Elaboration Data_Time Delay Neural Network (TDNN)
2.3. Experiments
3. Results
4. Conclusion
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1. Introduction
Automation systems in the automotive are the
trend
the next time
Autonomous vehicles still require the presence
of the human
The driver remains one of the most critical elements
in terms of accident
Propose the BCI system based on an EGG
and calculate a TDNN classification model
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1. Introduction
The main objective is to tackle the problem
of driver’s arms movements detection and
recognition
Classify the acquired data with a TDNN
Using EEG signals
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2.1. Test Drive
Signals were recorded from
namely F7, FZ, F8, C4, C3, CZ.
Each driver test has been performed three times
by a person who was 30 years old with a driving license
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2.2. Elaboration Data_TDNN
The elaboration data was performed in Matlab
R2020b
The filter is 0.167 Hz
Use for the EEG signals to
remove the direct current
shift
Three different analysis have been realized
by the implementation of time delay neural network
(TDNN)
TDNN used for applications in EEG signal analysis
TDNN classified finger movements with a recognition rate of 93.02 %
with have 10-time delays and 4 hidden fields
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2.3. Experiments
The first test is related to the classifier generation
by the Levenberg Marquardt algorithm based TDNN
The increasing number of input data
To evaluate the relationship
between
Reflect recognition performance
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2.3. Experiments
NN4, NN5,
and NN6
Modifying the cost
function
To minimize the MSE
NN7,
NN8, and
NN9
By multiplying the objective
components
To increase the accuracy of the recognition
To identify the set of
parameters
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2.3. Experiments
To evaluate TDNN performance variations calibrated or
random
according to different values of the weights
associated to inputoutput relationship
function
of the network
The recognition accuracy
The computation time
To evaluated comparing for the performances
by the system and the TDNN
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3. Results
The EEG signal
The actual arm
movements
The average value is R = 0.74 and, in three cases, R >
0.8
The R index of each TDNN in the first
analysis
Does not show significant improvement of the
performances to the increasing number of input data
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3. Results
A strong correlation is verified
The NN9 in respect
to the NN6
The modified objective function generates better values
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for R
& MSE
The NN7 in respect
to the NN4
The NN8 in respect
to the NN5
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3. Results
The value R is greater than 0.5 in the 80 % of
cases
The MSE is lower than 0.5 in the 50 %
The recognition
accuracy for TDNNs
initialized
randomly
accurate
weights input
values
The results very
similar in the two
approaches
The TDNNs with
weight initialization appears
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The TDNNs with
random initialization
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4. Conclussion
Results demonstrated
a statistically relationship
The EEG signals
Participants’ motions
to rotate the steering wheel
Calculate a curvein the virtual driving
simulation environment
Have to perform more detailed studies to
verify the improvementsin the recognition accuracy of the TDNNs
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My Presentation
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