LeeHyeonBin1
KimGwangho1
KimJuHyeong2
KangYoungShin2
ParkCheolsoo2
-
(Department of Software Kwangwoon University/ Seoul, Korea {hyunbinkong, rhkd865}@gmail.com)
-
(Department of Computer Engineering, Kwangwoon University / Seoul, Korea
{kjoohyu, ysin0414}@gmail.com, parkcheolsoo@kw.ac.kr
)
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Keywords
EEG, Authentication, Bayesian optimization, Deep learning
1. Introduction
Personal authentication through biometrics with each person’s unique biological characteristics
is safer from theft, loss, or forgery than physical keys or passwords [1]. Authentication using electroencephalogram (EEG) signals has been studied because
of its guarantee of strong security. Unlike EEG, some other biometrics, such as fingerprint
and face recognition methods, are easy to steal [2,3]. An EEG records the brain activities on the scalp, which are the averaged electrical
responses of neurons in the brain [4]. EEG signals have unique patterns corresponding to each person, making it suitable
as an authentication system. On the other hand, EEG signals can be affected by caffeine
[5], health conditions, or movement artifacts, such as eye-blinking, or biting [6]. These impediments reduce EEG power in specific frequency bands. In addition, EEG
signals are nonstationary; hence, they can vary slightly every day [7]. These can hinder the implementation of an EEG-based authentication system. A convolutional
neural network (CNN) model was designed as an authentication task and trained day-to-day
to overcome these problems. Using 1-D CNN, the time-series EEG features for each person
could be extracted, and the maximum pooling can control the number of parameters to
prevent excessive parameter concentration on the dense net [8]. The Bayesian optimization method was applied to search for the optimal structure
of the CNN model [9]. Although manual searches, grid searches, and random searches are conventionally
utilized for CNN model design, Bayesian optimization helps find the hyperparameters
efficiently, considering the experimental results.
2. Background
This section describes the EEG and Bayesian optimization.
2.1 EEG
In neurophysiology, EEG signals are used as indicators to monitor the changes in brain
activity, both spatially and temporally [10]. Its response to brain neural activities shows certain patterns varying with the
states of consciousness and mental activities [11]. Therefore, EEG signals indicate the level of brain activity as an objective indicator
to determine if the brain's activity increases or decreases. Generally, EEG signals
are decomposed into delta, theta, alpha, beta, and gamma waves, depending on the frequency
band [12]. A delta wave is a brain wave in the 0.5 to 4Hz range, which is dominant in deep
sleep with a fully relaxed brain function. Theta waves refer to EEG signals in the
4 to 8 Hz band, generally referring to the mid-state of body and consciousness, sleepiness,
and waking. The alpha wave, in the 8 to 13 Hz band, is the basic wave that reflects
the stability of the brain in neurophysiology with EEG signals. This wave has been
used traditionally to determine the functional state of the left and right hemispheres
of the brain for human behavior because it is less affected by hybrid waves. Beta
waves are EEG signals in the 13 to 30 Hz band that appear in awakening, active, and
stressed states and are also affected by auditory, tactile, and emotional stimuli.
Finally, the gamma wave is a brain wave that occurs relatively frequently in the frontal
and parietal lobes under intense anxiety and excitement with external consciousness
in the 30-50 Hz band [13].
2.2 Bayesian Optimization
The search for the optimal hyperparameters in a deep neural network model is the key
to improving its performance. Because of the importance of hyperparameters, many methods
have been used to optimize them, such as manual search, grid search, and random search
[14]. In Bayesian optimization, an objective function is handled as a random function
and places a prior probability distribution. The distribution estimates the change
in function by evaluating performance. The distribution is updated post-distribution
on the function to collect the next query point. The function is estimated from the
query points collected by the Gaussian process. This method can yield the optimal
value from the estimated function. The hyperparameters are optimized by repeating
this process.
3. Materials and Methods
This section describes data acquisition, preprocessing, model, and training.
3.1 Data Acquisition
EEG data sets were captured from 10 subjects, who were hungry, had not consumed caffeinated
foods or beverages for 12 hours and in a relaxed state with their eyes closed for
10 minutes, over a five day period. The EEG signals were captured with a sampling
rate of 600Hz and a 0.05Hz ~ 60Hz notch filter using a g.USBamp (from Guger Technologies,
Graz, Austria). The channels used for brain waves were Fp1 and Fp2 2 channels, as
shown in Fig. 1 [15].
Fig. 1. Montage of the 10-20 EEG electrodes.
3.2 Preprocessing
The recorded data were contaminated by motion artifacts and other ambient noises,
such as the power line noise. To reduce the noise, the raw data were bandpass filtered
using an FIR filter between 4 to 40Hz, as shown in Fig. 2.
Fig. 2. Raw data (left) and bandpass filtered data (right).
3.3 Model
A model was designed based on six 1D CNN layers with the LeakyReLU activation function.
Max pooling layers were applied to reduce the dimensions of the network output and
prevent excessive parameter concentrations on the dense net. A 0.3 dropout and batch
normalization layers were also added to prevent overfitting. The model was trained
using a binary cross-entropy loss and an Adam optimizer. Hyperparameters and the number
of layers of the model were optimized using Bayesian optimization, whose parameters
and search spaces are listed in Table 1.
The search step of Bayesian optimization was 16. The numbers of total parameters and
trainable parameters were 70,001 and 69,777. Fig. 3 shows t}he finally optimized model.
Table 1. Search spaces of the Bayesian optimization process.
|
Layer
|
Search space
|
|
Input shape
|
600(Hz) x 3(sec)
|
|
Conv1D
|
16 ~ 128
|
|
MaxPooling1D
|
2
|
|
Conv1D
|
16 ~ 128
|
|
MaxPooling1D
|
2
|
|
Conv1D
|
16 ~ 128
|
|
MaxPooling1D
|
2
|
|
Dropout
|
0.3
|
|
BatchNormalization
|
-
|
|
Conv1D
|
16 ~ 128
|
|
MaxPooling1D
|
2
|
|
Conv1D
|
16 ~ 128
|
|
MaxPooling1D
|
2
|
|
Conv1D
|
16 ~ 128
|
|
MaxPooling1D
|
2
|
|
Dropout
|
0.3
|
|
BatchNormalization
|
-
|
|
Flatten
|
-
|
|
Dense
|
16 ~ 128
|
|
Dense
|
16 ~ 64
|
|
Dense
|
1
|
|
Activation
|
softmax
|
Fig. 3. Optimized CNN model.
3.4 Training and Testing Process
The validation set for updating the network weights included 10% of the data recorded
each day. For the incremental learning each day, t}he training data sets were prepared
from the 1$^{\mathrm{st}}$ day, from the 1$^{\mathrm{st}}$ to the 2$^{\mathrm{nd}}$
day, from the 1$^{\mathrm{st}}$ to the 3$^{\mathrm{rd}}$ day, and from the 1$^{\mathrm{st}}$
to the 4$^{\mathrm{th}}$ day. The test data sets were prepared using the recordings
from the 2$^{\mathrm{nd}}$ to the 5$^{\mathrm{th}}$ day. The training and testing
were conducted in an inter-subject manner.
Table 3. Averaged Testing Results.
|
Test data
|
Train data
Metrics
|
Day1
|
Day1~
Day2
|
Day1~
Day3
|
Day1~
Day4
|
|
Val
|
accuracy
|
95.66%
± 2.75%
|
96.65%
± 3.08%
|
96.91%
± 2.20%
|
96.64%
± 1.97%
|
|
precision
|
85.80% ±15.95%
|
87.89% ±10.86%
|
96.53% ±5.07%
|
87.51% ±13.14%
|
|
recall
|
65.88% ±29.01%
|
75.95% ±23.43%
|
72.87% ±17.62%
|
80.65% ±16.29%
|
|
Day2
|
accuracy
|
93.52% ±3.69%
|
-
|
-
|
-
|
|
precision
|
62.95% ±31.99%
|
-
|
-
|
-
|
|
recall
|
45.80% ±30.83%
|
-
|
-
|
-
|
|
Day3
|
accuracy
|
92.50% ±3.29%
|
93.22% ±3.43%
|
-
|
-
|
|
precision
|
54.94% ±28.29%
|
69.41% ±17.12%
|
-
|
-
|
|
recall
|
42.25% ±32.71%
|
53.41% ±28.32%
|
-
|
-
|
|
Day4
|
accuracy
|
92.60% ±3.37%
|
93.31% ±3.49%
|
93.94% ±3.25%
|
-
|
|
precision
|
60.45% ±24.34%
|
69.91% ±17.64%
|
80.60% ±16.95%
|
-
|
|
recall
|
43.01% ±31.54%
|
55.51% ±28.77%
|
47.70% ±28.48%
|
-
|
|
Day5
|
accuracy
|
91.34% ±3.18%
|
92.76% ±3.25%
|
93.26% ±2.64%
|
93.23% ±2.73%
|
|
precision
|
65.64% ±21.36%
|
73.80% ±14.49%
|
79.61% ±8.09%
|
71.31% ±12.55%
|
|
recall
|
36.13% ±25.65%
|
51.44% ±23.10%
|
44.95% ±28.21%
|
57.65% ±28.82%
|
4. Results
The performance was measured using three metrics: accuracy, precision, and recall
which were calculated using Eqs. (2)-(4)
where $\textit{TP, TN, FP,}$ and $\textit{FN}$ denote the true positive, true negative,
false positive, and false negative, respectively. Ten different models were designed
for each subject as a binary classifier: self-versus the others. Table 3 lists the results with the mean and standard deviation. Each number represents the
averaged result across all subjects. The 1$^{\mathrm{st}}$ row elaborates the training
dataset prepared from the 1$^{\mathrm{st}}$ day, from the 1$^{\mathrm{st}}$ to the
2$^{\mathrm{nd}}$ day, from the 1$^{\mathrm{st}}$ to the 3$^{\mathrm{rd}}$ day, and
from the 1$^{\mathrm{st}}$ to the 4$^{\mathrm{th}}$ day. The 1$^{\mathrm{st}}$ column
denotes the validation dataset (the same day dataset as the training dataset}) and
testing dataset, which includes the data recorded on the next day of the training
dataset, such as the 2$^{\mathrm{nd}}$, 3$^{\mathrm{rd}}$, 4$^{\mathrm{th}}$, and
5$^{\mathrm{th}}$ days.
The results of the validation data showed the training performance of the model using
the same day data; all showed an accuracy of more than 90% across all 10 subjects.
As shown in Table 3, t}he performance decreased when the recording day of the testing dataset was far
from the day of the training dataset. Compared to the standard deviation of the accuracy,
those of the precision and recall were relatively higher.
To assess the improvement in performance with increasing training dataset size, the
variations in the result with additional training data on the following day were calculated,
as listed in Table 4. Overall, the incremental learning of the EEG signals day-to-day enhanced the authentication
performance significantly with a decreasing standard deviation.
Table 4. Variations with an additional training dataset.
|
Test data
|
Train data
Metrics
|
Day1~
Day2
|
Day1~
Day3
|
Day1~
Day4
|
|
Day2
|
accuracy
|
+0.99%
|
+0.26%
|
-0.27%
|
|
precision
|
+2.07%
|
+8.63%
|
-9.02%
|
|
recall
|
+10.06%
|
-3.07%
|
+7.78%
|
|
Day3
|
accuracy
|
+0.72%
|
-
|
-
|
|
precision
|
+14.47%
|
-
|
-
|
|
recall
|
+11.17%
|
-
|
-
|
|
Day4
|
accuracy
|
+0.72%
|
+0.63%
|
-
|
|
precision
|
+9.47%
|
+10.69%
|
-
|
|
recall
|
+12.50%
|
-7.80%
|
-
|
|
Day5
|
accuracy
|
+1.42%
|
+0.50%
|
-0.03%
|
|
precision
|
+8.15%
|
+5.81%
|
-8.30%
|
|
recall
|
+15.30%
|
-6.48%
|
+12.69%
|
5. Conclusion
This paper reported a feasible way to incrementally train nonstationary EEG data for
personal authentication using 1D-CNN and Bayesian optimization. The proposed neural
network model yielded an averaged accuracy, precision, and recall of 93.23%, 71.31%,
and 57.65%, respectively. The different conditions of the subjects every day and EEG
deformation could affect the performance. For future work, controlling more variables
and additional recordings will be needed to improve the result.
ACKNOWLEDGMENTS
This research was supported by the MIST (Ministry of Science and ICT), under the
National Program for Excellence in SW (2017-0-00096), supervised by the IITP(Institute
for Information & communications Technology Promotion)
REFERENCES
Jain A. K., Ross A., Pankanti S., Jun. 2006, Biometrics: A Tool for Information Security,
IEEE Transactions on Information Forensics and Security, Vol. 1, No. 2, pp. 125-143
Marcel S., Millan J. R., Apr. 2007, Person Authentication Using Brainwaves (EEG) and
Maximum A Posteriori Model Adaptation, IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 29, No. 4, pp. 743-752
Ashby C., Bhatia A., Tenore F., Vogelstein J., 2011, Low-cost electroencephalogram
(EEG) based authentication, in 2011 5th International IEEE/ EMBS Conference on Neural
Engineering
Sanei S., Chambers J., 2007, EEG Signal Processing
Dimpfel W., Schober F., Spüler M., Mar. 1993, The influence of caffeine on human EEG
under resting condition and during mental loads, The Clinical Investigator, Vol. 71,
No. 3
Abo-Zahhad M., Ahmed S. M., Abbas S. N., Oct. 2016, A new multi-level approach to
EEG based human authentication using eye blinking, Pattern Recognition Letters, Vol.
82, pp. 216-225
Marcel S., Millan J. R., Apr. 2007, Person Authentication Using Brainwaves (EEG) and
Maximum A Posteriori Model Adaptation, IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 29, No. 4, pp. 743-752
Wang M., El-Fiqi H., Hu J., Abbass H. A., Dec. 2019, Convolutional Neural Networks
Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse
Human States, IEEE Transactions on Information Forensics and Security, Vol. 14, No.
12, pp. 3259-3272
Močkus J., 1975, On Bayesian methods for seeking the extremum, Optimization techniques
IFIP technical conference, pp. 400-404
Sun Y., Lo F. P.-W., Lo B., Jul. 2019, EEG-based user identification system using
1D-convolutional long short-term memory neural networks, Expert Systems with Applications,
Vol. 125, pp. 259-267
Zeynali M., Seyedarabi H., Aug. 2019, EEG-based single-channel authentication systems
with optimum electrode placement for different mental activities, Biomedical Journal,
Vol. 42, No. 4, pp. 261-267
Wang M., El-Fiqi H., Hu J., Abbass H. A., Dec. 2019, Convolutional Neural Networks
Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse
Human States, IEEE Transactions on Information Forensics and Security, Vol. 14, No.
12, pp. 3259-3272
Wang J., Barstein J., Ethridge L. E., Mosconi M. W., Takarae Y., Sweeney J. A., Sep.
2013, Resting state EEG abnormalities in autism spectrum disorders, Journal of Neurodevelopmental
Disorders, Vol. 5, No. 1
Bergstra J., Bardenet R., Bengio Y., Kgl B., 2011, Algorithms for Hyper-Parameter
Optimization, Neural Information and Processing Systems
Jurcak V., Tsuzuki D., Dan I., Feb. 2007, 10/20, 10/10, and 10/5 systems revisited:
Their validity as relative head-surface-based positioning systems, NeuroImage, Vol.
34, No. 4, pp. 1600-1611
Author
Hyeonbin Lee is in the BSc Program at the Department of Software at Kwangwoon University,
Seoul, Republic of Korea. His research interests include security, machine learning
algorithms, deep learning, and reinforcement learning.
Gwangho Kim is in the BSc Program at the Department of Software at Kwangwoon University,
Seoul, Republic of Korea. His research interests include machine learning algorithms,
deep learning, specifically, AutoML, and explainable AI
Juhyeong Kim is in the MSc Program at the Bio Computing & Machine Learning Laboratory
(BCML) in the Department of Computer Engineering at Kwangwoon University, Seoul, Republic
of Korea. He received a BSc from the Department of Computer Engineering, Kwangwoon
University, Seoul, Republic of Korea, in 2019. His research interests include machine
learning algorithms, specifically, deep learning, reinforcement learning, and automated
machine learning (AutoML)
Youngshin Kang graduated from the computer engineering department at Far East University
in Chungbuk, South Korea. Her research interests include machine learning, deep learning
optimization, biological signal, and signal processing. She is particularly interested
in brain engineering.
Cheolsoo Park is an associate professor in the Computer Engineering Department
at Kwangwoon University, Seoul, South Korea. He received a B.Eng. in Electrical Engineering
from Sogang University, Seoul, and an MSc from the Biomedical Engineering Department
at Seoul National University, South Korea. In 2012, he received his Ph.D. in Adaptive
Nonlinear Signal Processing from Imperial College London, London, UK. He worked as
a postdoctoral researcher in the Bioengineering Department at the University of California,
San Diego, USA. His research interests are mainly in machine learning and adaptive
and statistical signal processing, with applications to brain-computer interfaces,
computational neuroscience, and wearable technology.