Optimal EEG Locations for EEG Feature Extraction with Application to User’s Intension using a Robust Neuro-Fuzzy System in BCI

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https://doi.org/10.13160/ricns.2018.11.4.167

Optimal EEG Locations for EEG Feature Extraction with Application to User’s Intension using a Robust Neuro-Fuzzy System in BCI

Chang Young Lee, Ibrahim Aliyu, and Chang Gyoon Lim^†

Abstract

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification.

In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

Keywords: BCI (Brain Computer Interface), EEG (Electroencephalogram), Classification, Neuro-Fuzzy System, Robust Electrode Location.

1. Introduction

A human brain is a complicated and difficult orga- nization and it is epochal in time and space. The number of patients with nervous system impairment is increasing due to an increase in the elderly population and stress in daily life and social life. In paralysis patients, basic activities are restricted due to difficulties in communication. In order to overcome these problems, it is required to use a real-time interpretation of the brain signal (brain wave) by connecting the human brain to the machine, or a fusion technique for enhancing the human ability by inputting and modulating external information. In other words, Brain-Computer-Interface (BCI) using Electroencephalogram (EEG) signal should be able to provide communication channel between human and machine. The cognitive function of the person is converted into a control command so that the user

can interact with the external device. This can be used as an alternative to communication for patients suffering from severe motor impairment^[1,2].

The basic operation of the BCI is to record cerebral bioactivity through electrodes that differentiate between mental tasks. These systems capture user intent without physical action and translate it into interactive com- mands. The brain waves are first analyzed and classified to do it. It can be realized by estimating the mental work that the subject imagines. Since the computer and the machine are controlled based on the estimated mental work, it can be regarded as a natural activity of human- machine communication. Thus, the BCI system can help a variety of everyday tasks for people who have severe disabilities or mobility restrictions.

Fig. 1 shows a typical BCI system architecture. The system mainly consists of three parts as follow:

① Electroencephalogram (EEG): Measured using noninvasive device, and the measured data is stored in a database

② Signal processing and machine learning: Machine learning for various signal processing and recognition to extract features to recognize user's intention

Major in Computer Engineering, Chonnam National University, Yeosu

†Corresponding author : cglim@jnu.ac.kr

(Received : December 9, 2018, Revised : December 14, 2018, Accepted : December 15, 2018)

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③ Device: Device such as robot that reflects user's intention

Electroencephalogram (EEG) is a method of obtaining the electrical activity of the brain as an analog signal by using an instrument called an electroencephalogram.

Recent studies have focused on non-invasive approaches based on brain waves. It is a bio-potential signal with an amplitude of several mV and a frequency of less than 250 Hz. Analysis of the EEG signal has been done in many ways mainly depending on the type of signal to be analyzed and the information to be retrieved^[3,6]. A conductive gel is used and an electrode is placed on the scalp to acquire the brain signals. The received EEG signal is shown in microvolts and amplified through an amplifier. The amplified signal is delivered to the computer via an analog-to-digital (AD) converter.

After obtaining the EEG signals, the next step is to transformed the signals into appropriate domain in order to extract useful features for the signal classification.

Many approaches such as time domain, frequency domain and time-frequency domain can be used to transform the raw EEG data. Since brain waves are not usually fixed, it is best to use time-frequency domain methods such as wavelet transforms as average values for feature extraction^[11]. In particular, the time-frequency representation of the EEG signal for two mental states can be obtained using Discrete Wavelet Trans- form (DWT^)[7,8]. In DWT, the signals are decomposed into different frequency bands and DWT coefficients obtained in the process are used as feature vectors in the classification process.

The wavelet transform provides a more flexible time- frequency representation of the signal using variable-

sized windows. In wavelet transform, the long time window is used to obtain a fine low frequency resolution and the short time window is used to obtain high frequency information. Therefore, wavelet transform provides accurate frequency information at low frequency and accurate time information at high frequency. This is suitable for irregular data pattern analysis, such as impulses, where wavelet transforms occur at various time instances.

Artificial Neural Networks (ANN) are statistical learning algorithms based on biological neurons. The algorithm is a method of generating an extension of the brain dedicated for decoding the user's activity for a particular application. It classifies the behavior of the user by extracting characteristics of the subject with respect to the data. ANN as a machine learning tool, loosely imitates the way the brain adapts and classifies input patterns based on reinforcement learning^[9]. It is employed to learning patterns of features in order to differentiate information.

Recently, there has been much progress in the field of automatic seizure detection using EEG. Detection methods generally use some rules to integrate time and spatial information extracted from primitive brain waves. The fuzzy logic system can provide a frame- work for dealing with pattern recognition problems whose decision boundaries are fuzzy with gradual class membership. These rules are based on expert inferences that make decisions about seizure detection. The rules are flexible and tolerate patient-to-patient variability in seizure and seizure activity. The advantage of this method is that it can design an expert rule-based interface between language information and functions for- mulated using quantitative measures^[10].

Despite the huge opportunity EEG signals provide for us to interprets brain signals, it is face with challenges.

The waveform of the brain depends on the position of the brain from which the signal is obtained and the state of the person. Also, the frequency of the wave depends on measurement standard of the apparatus and how the measurement is performed^[12].

In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of Fig. 1. The structure of a typical BCI system. This system

serves as an interface for communication between humans and machines. It consists of the EEG, which is the source of the data, the processing and machine learning part, and the part that receives and reflects the result with action.

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fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. The EEG data for four actions with right arm were collected and used for the experiments. The actions include Bend Lower Limb (BLL), Release Lower Limb (RLL), Grasp Hand (GH), and Open Hand (OH). The result of this research could play an import- ant role in communication channels for paralyzed people with movement disorders or for exercise for rehabilitation.

This paper is organized as follows: the introduction, including the literature review is presented in Section 1;

Section 2 describes related background for the work, which has great impact on the system; the design of an intelligent neural fuzzy system for EEG classification is presented in Section 3; in Section 4, we present the implementation of the system and show results related to the proposed model, before drawing some conclu- sions on Section 5.

2. Background of Study

2.1. EEG (Electroencephalogram) Signal Electroencephalogram (EEG) is an electrical signal that is noninvasively measured by an electrode attached to the brain surface. In 1875, British physiologist Rich- ard Caton was first known to record the galactic system of weak electrical activity from the cerebral cortex of a rabbit or monkey for the first time. EEG may be different depending on the state of mind and body, the state of activity of the brain, and the situation. Neuro- physiologic measurement of brain electrical activity measures and records brain waves through the electrodes attached to the scalp^[13]. Sometimes the electrodes are attached to the cerebral cortex. This is used to diag- nose brain damage or other diseases or to determine the condition of the brain.

The EEG signal can be viewed as a spectrum through frequency analysis. The spectral analysis is judged to be a linear combination of simple vibration waves in which EEG vibrates at a specific frequency. According to this signal, each frequency component is disassembled and the corresponding force is displayed. There exists a difference according to the frequency and amplitude when

analyzing brain waves using spectral analysis. The waveform of the EEG from the human brain comes out at a frequency of 0 ~ 50 Hz, with an amplitude of about 2 ~ 200 μV. There is a standard index that indicates the ability to measure each signal at each location where the electrode is attached through EEG characterization.

This index is called the EEG 10-20 electrode index and is shown in Fig. 2^[14]. EEG is measured by attaching Electrode to the head using standard index. In general, a 10-20 electrode standard indicator can determine the position of the electrode and measure the corresponding signal.

2.2. Artificial Neural Network as a System Structure

The human brain is structurally and functionally very complex, consisting of about 100 million neurons. They are tightly interconnected and have complex structures and intelligent forms that are difficult to implement by any artificial system. Several mathematical models have been developed to represent the interconnections of these neurons. Artificial neural network (ANN) attempt to reproduce learning ability among human brain potential. The value of the input signal and the associated weight determines the neuron output. Artificial neurons are mathematical models of neurons and can be regarded as the basic unit of artificial neural networks.

A perceptron structure contains a weight and an activa- tion function associated with a series of inputs.

The single layer perceptron structure is well suited for problems that can be linearly distinguished. However, Fig. 2. EEG 10-20 system position^[14]. This indicates the location of the scalp electrode and is it the internationally accepted method. This standard method was developed to ensure reproducibility. The relationship between the location of the electrodes and the basal cortex is shown.

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it is difficult to use if it can be separated nonlinearly.

In this case, a multilayer perceptron containing at least one hidden layer should be used. This topology solves the nonlinearly separable pattern classification problem and is also used as a general purpose function generator.

Multilayer perceptron consists of two stages: training and execution. In this network topology, you cannot use the delta rule directly for training. Therefore, algorithms widely used in multi-layer perceptron network learning use error backpropagation designed by modifying the delta rule. This learning method is more complex than the perceptron network and is a representative algorithm of teacher learning^[15].

As shown in Fig. 3, the general artificial neural network structure consists of an input layer, a hidden layer, and an output layer. The input layer consists of the neurons corresponding to the input variable and is connected to the neurons of the hidden layer. The hidden layer is the layer between the input layer and the output layer and it learns the relation embedded in the data or fact through learning. The number of hidden layers and the number of neurons in the layer affect the performance of neural networks. The input signal of the hidden layer is transmitted to the output layer. The output layer converts the processing result of the input signal into a numerical value or a value and outputs a value.

2.3. Fuzzy Theory for a Rule-based Technology Fuzzy theory treats fuzzy sets and fuzzy logic

expressing uncertain or ambiguous elements as one quantity. Symbol and numerical processing have wider meaning than general logic. The main goal of fuzzy logic is to perform systematic computations to handle more approximate reasoning types than exact. It is con- ceptually easy to understand and scalable for any given system. The tolerance range for inaccurate information is widely accepted. The adaptive fuzzy algorithm can be easily implemented by creating a fuzzy system corresponding to a set of input/output information^[16].

A fuzzy set is a collection of elements representing the degree to which an element belongs to the set. The membership function is used to determine the degree of affiliation. It is a function that indicates the degree to which an element belongs to a set. The membership function has a value between 0 and 1, and the set can be represented by the membership function notation^[17]. Let μ_A (x) denote the membership function indicating the degree of having a specific property with respect to an arbitrary element x belonging to X, the fuzzy set A can be expressed as (1).

A = {(x, μ_A (x))|x∈X} (1)

If the entire set is continuous and the range of membership is infinite, the fuzzy set can be expressed as (2).

(2) The fuzzy proposition is expressed by the fuzzy con- cept of the description part. The conditional and conclusion fuzzy propositions are called fuzzy rules. In other words, the fuzzy rule is an approach to analyze the system and outlines the outline. This method takes into account the method of inferring the variable y when any value is given to the variable x based on the rules of this shape. In this paper, we propose a fuzzy rule like (3) because it is MIMO (Multi-In-Multi-Out) with a large number of input and output layers.

Rule:

IF x1 is A1 and x2 is A2 and ….. xn is An

THEN y1 is B1 and y2 is B2 and ….. ym is Bm (3) Since it is a plurality of outputs according to a plurality of inputs, it is possible to analyze by expressing various values. The fuzzy controller consists largely of

A μ_A( )x ---x

=∫

Fig. 3. General structure of an artificial neural network.

This structure is composed of input layer, hidden layer, and output layer as general neural network structure.

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fuzzification, fuzzy reasoning including fuzzy rule base, and defuzzification.

In Fig. 4, the fuzzy operation is performed to convert the crisp numerical information into the fuzzy set. That is, the general numerical value is converted into fuzzy data. Fuzzy inference is an operation for reasoning about fuzzy input values and can use deduction and inductive reasoning. The fuzzy data obtained from the inference should be converted into the crisp value for the use in the real world. Defuzzification is an operator that converts the fuzzy value represented by the fuzzy set to the original numerical value. This is the process of determining the most effective control output from the probability distribution obtained from the fuzzy inference by the fuzzy rule.

2.4. Genetic Algorithm for Optimizing Parameters A genetic algorithm is based on the biological genet- ics theory of nature. This is a parallel and global search algorithm, which is based on survival theory. Genetic algorithm represents possible solutions to the problem to be solved in a predetermined form of data structure.

In the genetic algorithm, the properties of a solution are displayed through a data structure such as an array of numbers or a string. The data structure represented by the solutions is a gene and evolution is called the process of creating a good solution by transforming it.

Through the fitness function, how well the solution

can be achieved is calculated. It is possible to calculate how well this solution is to be achieved through the fitness function. The fitness function is a function for eval- uating how much the solution is suitable as the solution of the given problem. If a genetic form is defined for a problem, a new solution can be created from an exist- ing solution by combining genes of certain solutions together. The crossover operation is a typical combination operation. The solution that is created after selecting the best solution are subjected to crossover operations in order for the solution to inherit good characteristics of the genes. A fitness function can be used in selecting a good solution. If the probability of choos- ing a solution with a higher fitness is increased, the probability that a solution with a better gene will hand over its gene to the next generation becomes higher. So the next generation of solutions are getting closer to the optimal solution. Even if we do not leave offspring through crossbreeding, we can create a new gene through mutation and hand it over to the next generation.

Fig. 4. Fuzzy Inference System. The strategy of the fuzzy system determines the optimal number of fuzzy rules using clustering from feature data.

Fig. 5. Flow chart of a genetic algorithm including major operators. This algorithm is used to optimize the parameters found in the structure phase.

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The initial solution group only serves as the initial requirement for finding future solutions. Generally, genes are randomly generated to form an initial population. When an initial solution group is constructed, a set of solutions of the next generation is generated through crossover of the inner solutions. Repeatedly repeating the generations, they become more and more correct^[18]. As shown in Fig. 5, a genetic algorithm consists of major operations such as selection, intersection, and variation. The trait of the gene is first determined and the evaluation is carried out according to the fitness function. We will evolve the gene household number through selection, crossover, and mutation methods, and then check the fitness evaluation criteria. When the fitness evaluation criterion is satisfied, the algorithm is ter- minated^[19].

2.5. Wavelets to be used for Feature Extraction A wavelet is a wave-like vibration accompanied by an amplitude that repeats, increase and decrease around zero. Wavelets have useful features for signal processing. The wavelet can be used to extract information from an unknown signal in combination with a known signal through a convolution technique. It is often used to extract useful information from audio signals or images as well as from a variety of other types of data.

An additional series of wavelets is needed to fully analyze the data. Thus, a set of secure wavelets is useful for wavelet-based decompression algorithms designed to minimize loss and restore raw information^[20].

Wavelet transforms is made up of a set of special signals that model signals, systems, and processes. It is

expressed as an arbitrary waveform through a scale in which one small waveform is used as a pattern to be transited or enlarged or reduced. The basic formula of wavelet transform is shown in (4).

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The set of functions obtained by mutating and scaling the number of entities defined by ψ(x) is called wavelet.

a adjusts the size of the wavelet basis as a magnitude factor. b represents the variation on the time axis and changes this value to position the wavelet basis at the desired location. If a is small, it is located in a narrow area on the time axis and the frequency axis occupies a large area. cjk denotes a wavelet coefficient.

These instructions have been produced using a 10.5 point Times Roman font. Title and subtitle are written in bold-faced characters.

3. Design of an Intelligent Neural fuzzy System for EEG Classification

3.1. Preparing EEG Data Set for User’s Intention Recognition

We used an Emotiv headset to collect EEG signals ^[12]. The EEG data collected are based on four action of the right arm. The actions include Bend Lower Limb (BLL), Release Lower Limb (RLL), Grasp Hand (GH), and Open Hand (OH). The brain waves were recorded for 4 seconds to make the mind and body movement as

Wψ f

[ ] a b( , ) 1

--- ψ x ba – ---a

⎝ ⎠

⎛ ⎞f x() xd

= ∫

cjk=[Wψ f] 2( ^–^j,k2^–^j)

Fig. 6. EEG data collection process and intelligent neural fuzzy system for EEG classification. The right side shows the EEG data extraction process. The extracted data is transferred to the left for the classification. In the learning process, the fuzzy system is used to identify the system structure and the genetic algorithm is used to optimize the system. The learned system is processed through the fuzzy system immediately without executing this step in the execution process.

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close as possible.

About 4000 ~ 5000 data were collected at one time when the intensity of brain waves is the most accurate.

A total of 16 data signals were measured for the experiment. The average value of each signal was calculated and stored as one data after the wavelet transform of the EEG data extracted at one time.

The process of BLL and RLL were performed by hand. In the process of GH and OH, the arm is in an upright position. Table 1 shows information on experimental data for user’s intension. Data were classified into training data and test data to facilitate learning.

70% were used as training data and the rest were used as testing data. Selection criteria were set at random.

The learning was repeated to reduce the variance of the learning rate according to the difference of information.

Table 2 shows the breakdown of training data and testing data. Information about each behavior is given to compare and analyze the learning results according to the EEG electrode region. Since the hands and arms are closely connected to each other in the body, they are represented by three outputs. t denotes the target output value. The number of output nodes in the neural network structural expression will be three. When t1 is 0, it means when the hand has acted. On the other hand, it indicates 1 when the arm has acted. When t2 is 0, it means ‘fold’ and when it is 1, it means ‘spread’. t3 has the opposite meaning to t2.

3.2. The Proposed System Modeling using Neural Fuzzy Approach

The proposed system is modeled to classify EEG of signals using neural fuzzy approach. First, the EEG signal is extracted using a headset. Emotiv headset was used for brain wave collection and wavelet method was used for data processing.

Fig. 7 shows the proposed system from data extraction to learning process. Here, neural networks are structural expressions. The system for actual decision making is fuzzy based. Genetic algorithms are used to optimize the system. After setting the criterion for determining EEG measurement, EEG is measured for a certain time.

Through the wavelet transform of the measured EEG, features including the user 's intention are extracted. The processed data is stored in the database together with the data before machining.

FCM (Fuzzy c-Means) clustering technique is used for grouping the given data. The number of clusters is determined through an optimal cluster efficacy analysis.

The number of determined clusters is determined by the number of fuzzy rules proposed in this study. Once the optimal number of rules is determined, the mean value and the standard deviation value for each cluster are obtained as a result. Only the values related to the standard deviation are used as weights in the system.

The genetic algorithm optimizes the weights including the values obtained from the cluster analysis of the features extracted from EEG data. Each chromosome is represented by standard deviation values and weights.

Initial weight values are set at random. In this case, since the membership function is set to asymmetric Gaussian, the standard deviation values are assigned to chromosome and managed separately on the left and on the right.

The initial standard deviations are the values obtained from the cluster and the left side and right side values are assigned the same value at the beginning. Once the chromosome setup is complete, optimization is performed through the main operations of the genetic algorithm. At this time, the fitness function is applied to confirm whether the termination condition is satisfied.

The fitness function is performed through the fuzzy system, which can be verified through the results of the defuzzification. The value of the error rate is expressed as the result of the fitness function. The training is performed until the fitness function satisfies the termination condition.

Table 1. Periodic table of elements

Action Number of data

BLL 150

RLL 150

GH 150

OH 150

Total 600

Table 2. The extracted training and test data using an Emotiv headset to obtain EEG signals

Action Number of training data

Number of test data

Target output of user’s intension t1 t2 t3

BLL 105 45 0 0 1

RLL 105 45 0 1 0

GH 105 45 1 0 1

OH 105 45 1 1 0

Total 420 180

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3.3. EEG Signal Analysis for Feature Extraction The features of the EEG signal for each command is extracted in order to grasp the user's intention. The wavelet transform can be used to analyze the transformed signal in the desired frequency band by dividing the frequency band into the high frequency and the low frequency by multiplying the input signal. The wavelet technique used to process the acquired EEG signals is expressed as arbitrary waveform by modifying the frequency. The magnitude of the wavelet extension constant decreases rapidly when the signal is large. Very accurate local representations are possible and signal characteristics can be separated. Since there is only one wavelet, it must be designed according to each application.

(5) Eq. (5) is a basic formula of wavelet transform. The conversion of an EEG signal into a discrete wavelet can

be obtained by discretizing the scaling element (a) and the transition element (b). The down-sampling process is performed by dividing the signal into two bands through a high-pass filter and a low-pass filter and tak- ing only half of the divided data. This process is repeated to decompose to the desired level^[22].

Fig. 8 and Fig. 9 show an example of wavelet synthesis and decomposition of 3-step signal respectively.

In the process of reconstructing the three-level signal, the number of stages of the solution is chosen as the dominant frequency component of the signal. The frequency range used for EEG is 0 to 50 Hz. Condition 2ⁿ<50 must be satisfied, so that when n is less than 5, it converges to the condition closest to 50. Fig. 10 shows the wavelet decomposition process in the 4^th step.

Table 3 shows wavelet decomposition according to EEG frequency.

The approximate coefficient L and the detailed coefficient H of each step are characterized. The basis function selection selects the wavelet transform through ψ a b( , ) x( ) 1

--- x t()ψ t ba – ---a

⎝ ⎠

⎛ ⎞ td

= ∫

Fig. 7. Intelligent Neural Fuzzy System with data processing.

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experiments. As a method of extracting features from the EEG, wavelet transform is used to analyze the original data of the EEG, and a 4-step wavelet constant is used. Here, we use four-level wavelet transform with the number of masks D4. The extracted wavelet coefficient denotes the EEG data distribution over time. The mean(µ), standard deviation (σ), and entropy (ε) of the range of EEG data over a certain period of time is

obtained as a means for expressing EEG data as a feature. Mean is used as a means of expressing EEG data.

The mean of each EEG electrode region is expressed.

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These instructions have been produced using a 10.5 point Times Roman font. Title and subtitle are written in bold-faced characters.

3.4. Membership Function

The membership function is used as a means of ver- bally expressing and conveying ambiguity in a given fuzzy set. The membership function used in this study is the Gaussian function expressed in (7).

(7) In general, left and right normal symmetry is used for the Gaussian function. In this study, we use asymmetric Gaussian membership functions such as (8), which can manage the left and right standard deviation separately, to improve system performance^[23].

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A(x) represents a Gaussian membership function. υ is the mean value belonging to each cluster in the rule. s is the standard deviation of a given membership func-

σ 1

N----∑_i=1^N (xi–μ)²^,^μ _N^----¹∑i=1^N ^xⁱ,

= =

ε⁼^–∑_i=1^N P x( ilog₂( )xi)

Aji( ) exi x_i–v_ji

( )²

– 2 σ( )² ---

⎝ ⎠

⎜ ⎟

⎛ ⎞

=

Aji( )xi e

x_i–v_ji

( )²

– 2 σ( )² ---

⎝ ⎠

⎜ ⎟

⎛ ⎞

xi≤vji

e

x_i–v_ji

( )²

– 2 σ( )² ---

⎝ ⎠

⎜ ⎟

⎛ ⎞

xi>vji

⎩⎪

⎪⎨

⎪⎪

⎧

= Fig. 8. 3-step wavelet synthesis.

Fig. 9. 3-step wavelet decomposition.

Fig. 10. 4-step wavelet decomposition according to EEG range.

Table 3. Wavelet Decomposition according to EEG frequency

Frequency Band Analysis level

Wavelet constant

α 8~12 Hz 3 H3

β 12~30 Hz 2 H2

θ 4~7 Hz 4 H1

γ 30~50 Hz 1 H1

δ 0.1~3 Hz 4 L1

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tion. Since the membership function management uses the asymmetric membership function, the left and right sides of the standard deviation are separately managed.

sr is the standard deviation for the left side based on the Gaussian function and sl is the standard deviation for the right side based on the Gaussian function. After computing membership function value, fuzzy inference is first defined using knowledge base to proceed with defuzzification. The value of the j^th rule for the given input vector is defined as:

(9) We define MIMO (Multi-In Multi-Out) fuzzy rule as (3). A fuzzy model rule with an output vector Y = {y1, y2, ……, yn} is defined by the n-dimensional input vector X = {x1, x2, …., xn}. We use (10) that is a center of gravity method to defuzzify in order to obtain the final result value. Computation is possible by predicting the final result according to the input value.

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3.5. Fitness Function and Chromosome Expression Fuzzy c-Means (FCM) clustering technique and cluster validation are used to determine the neural fuzzy system structure^[24]. Fuzzy c-Means Clustering performs

clustering by iteratively searching for a set of fuzzy clusters and the associated cluster centers that represent the structure of the data as best as possible is obtained.

The set of n vectors X is divided into c fuzzy groups, and the cluster center is searched in the same group as the cost function of non-similarity measurement is min- imized. The sum of membership in the dataset should always be 1.

The learned value can be confirmed according to the mean, standard deviation, and weight. The y value is extracted using non-fuzzification. The extracted value is compared with the original result value target (t), and error rate (E) is measured as (11).

(11) Table 4 shows genes composed of standard deviations and weights according to the system structure. S is the standard deviation, and W is the weight. sl is the left standard deviation in the Gaussian distribution and sr is the right standard deviation in the Gaussian distribution. c is the number of clusters divided. i is the number of input layers, and o is the number of output layers.

4. Experimental Results and Analysis for the User’s Intension Classification

4.1. Analysis of EEG Data

The frontal lobe and the parietal lobe in the brain area Uj=Aj1( ) Ax1 ⋅ j2( ) ………Ax2 ⋅ jn( )xn

y^k p=1 pⁿ U^kwpk

∑

n Uk p=1 p

--- U∑ , _p^k ∏^m_{i=1 ki}A xi

= =

E 1

n---∑_k=1ⁿ (tk–yk)²

= Table 4. Genes composed of standard deviations and weights

Chromosomes [S¹| S²| S³| S^c| W]

S¹ :

S² ..

S³

: : : : : : :

S^c W¹ W² W³

: : : : : : :

W^c

S_1l¹,S_1r¹ S_2l¹,S_2r¹ S_3l¹,S_3r¹ S_4l¹,S_r¹ S_il¹,S_ir¹ S_1l²,S_1r² S_2l²,S_2r² S_3l²,S_3r² S_4l²,S_r² S_il²,S_ir² S_1l³,S_1r³ S_2l³,S_2r³ S_3l³,S_3r³ S_4l³,S_r³ S_il³,S_ir³

S_1l^c,S_1r^c S_2l^c,S_2r^c S_3l^c,S_3r^c S_4l^c,S_r^c S_il^c,S_ir^c

W₁¹ W₂¹ W₃¹ W₄¹ W_C¹

W₁² W₂² W₃² W₄² W_C²

W₁³ W₂³ W₃³ W₄³ W_C³

W₁⁰ W₂⁰ W₃⁰ W₄⁰ W_C⁰

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are responsible for the actions under investigation in this experiment. In Fig. 11, the blue part is the frontal lobe and the yellow part is the parietal lobe. The frontal lobe is the widest part of the cortex and performs the most complex function. It extends from the front of the brain to the top of the head. It is the part that controls the body parts and solves problems related to attention. The parietal lobe extends from the top to the back of the brain and is responsible for high-level sensory processing and language processing functions. This part is responsible for the information and function of the muscles in the body as to how to move. Mainly responsible for the areas on the surface of the cortex that are physically sensitive to tactile, painful, and depressed.

The location of the data to be extracted is determined by comparing the positions of the emotional EEG electrodes corresponding to the regions of the brain belonging to the cerebral cortex. In the cases of Common Mode Sense (CMS) and Driven Right Leg (DRL), it is possible to belong to the parietal lobe or the temporal lobe depending on the head size of a person. We, therefore, excluded data related those regions in this research. Table 5 summarizes electrode location accord-

ing to the location of the cerebral cortex. The training data consists of the values of the EEG electrodes based on the frontal and parietal lobes.

4.2. Determine of the Number of Fuzzy Rules by using Optimal Cluster Evaluation

In order to determine the optimal number of clusters for given data, we set the maximum number of clusters to 10 and obtained the optimal clusters using validity.

Electroencephalogram (EEG) data were used for the experiment. The number of optimal clusters is the maximum value in the evaluation. In Fig. 12, the results of the optimal cluster evaluation show the best efficiency when the number of clusters is five. Therefore, the number of hidden layers and the initial parameter values of the proposed system are determined using this.

4.3. Experimental Results according to Electrode Location

We investigated how optimum solution can be obtained based on the position of the electrode. The left and right EEG signals were classified and the training was performed with each signal alone. First, the fuzzy system proposed in this paper was used to symmetri- cally divide the signals into left and right EEG signals.

The termination condition was set to 100 generations.

The Learning process uses the same EEG data. The training data and the test data are partition in advance into five randomly divided data sets each time the training is performed. 50 training iterations were performed on one data set and the average error rate, mean error rates and the standard deviations were obtained.

Table 6 and Fig. 13 show the results of 20 experiments using the values of the same points symmetri- Fig. 11. Cerebral Cortex Pattern^[25] and Cerebral Cortex

and Emotiv Electrode Location^[12].

Table 5. Electrode Location according to cerebral cortex location

Cerebral cortex Signal color

Electroencephalogram

left right

Frontal lobe Blue AF3,F3,F7, FC5

AF4,F4,F8, FC6

Parietal lobe Yellow P7 P8

Temporal Green T7 T8

Occipital lobe Red O1 O2

Fig. 12. Determining the optimal number of clusters by using FCM.

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cally on the left and right sides of EEG. Experimental results show that the error rate is less than 10% at position {F3, F7, FC5, F4, F8, FC6} in the 18th experiment and at {F7, FC5, F8, FC6} in the 13th experiment. The mean error rate in the 13th experiment was 9.29%. The 18th experiment showed the lowest error rate, which was 8.17%. Since the difference in standard deviation is not large, it can be confirmed that the data is uniform.

Training was performed using only brain waves belonging to the left brain and brain waves belonging to the right brain. The training process was carried out in the same way as the previous method. Fig. 14 shows the results of the comparison of the training mean error rates according to the left and right segmented data.

Experimental results have shown that the left brain is slightly better with average error rate of 9.94%, while the right brain gave the lowest average error rate of 10.51% at {AF3, F7, F3, FC5}.

Table 6. Training average error rate according to left and right symmetry data Exp.

No.

Electrode Location

1 2 3 4 5 Avg. Err.

Rate SD

Left Right

1 AF3 AF4 30.74 31.05 29.18 32.31 29.27 30.51 1.173

2 F7 F8 34.12 31.20 36.04 35.76 32.23 33.87 1.905

3 F3 F4 34.45 36.44 36.51 35.07 36.74 35.84 0.910

4 FC5 FC6 31.60 30.78 32.58 31.02 32.78 31.75 0.805

5 T7 T8 33.42 33.45 32.78 33.14 31.84 32.93 0.594

6 P7 P8 45.47 44.48 46.78 44.24 45.87 45.37 0.929

7 O1 O2 42.39 43.87 44.82 42.67 43.48 43.45 0.870

8 AF3,F7 AF4,F8 22.07 21.00 21.65 22.85 21.85 21.88 0.600

9 AF3,F3 AF4,F4 26.09 27.03 26.85 26.01 25.98 26.39 0.452

10 AF3,FC5 AF4,FC6 27.19 28.66 27.21 27.45 26.98 27.50 0.599

11 AF3,T7 AF4,T8 30.10 31.21 29.78 30.02 29.90 30.20 0.515

12 F7,F3 F8,F4 14.33 14.45 14.72 13.98 14.50 14.40 0.243

13 F7,FC5 F8,FC6 9.40 9.12 9.03 9.37 9.51 9.29 0.180

14 F7,T7 F8,T8 18.60 19.21 19.03 18.90 18.47 18.84 0.272

15 F3,FC5 F4,FC6 13.56 13.27 13.98 12.88 13.47 13.43 0.360

16 F3,T7 F4,T8 23.17 23.24 23.78 24.03 23.04 23.45 0.383

17 FC5,T7 FC6,T8 25.54 24.98 25.45 24.78 24.43 25.04 0.415

18 F3,F7,FC5 F4,F8,FC6 8.08 8.24 7.98 8.04 8.17 8.10 0.092

19 F3,F7,T7 F4,F8,T8 11.87 11.24 11.08 12.10 12.20 11.70 0.454

20 F7,T7,FC5 F8,T8,FC6 10.83 10.97 10.60 10.23 11.12 10.75 0.311

Fig. 13. Error rate when training using symmetric data.

Fig. 14. Comparison of training error rate with left and right segmentation data.

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Next, the experiment was carried out by increasing the termination condition to 1000 generations under the same condition in an environment as the previous experiment. According to the experimental results, as shown in Table 7, the training average error rate was less than 8%. When using the inputs measured at {F7, F8, FC5, FC6}, the best results were obtained at 5.91%.

Likewise, when using the test data, it showed the best result at 6.31%.

The results obtained when using the values obtained from {F3, F4, F7, F8, FC5, and FC6} as input data were 6.21% and 7.28% for {AF3, F7, F3, FC5}. According to the results of this experiment, both the training and the testing results were optimal when the data at {F7, F8, FC5, FC6} were used.

4.4. Comparison of Training Results with and without Wavelet Transform

In this experiment, we use wavelet transform to check the result of feature extraction at a given location.

Experiments were performed using EEG electrodes at the positions derived from the previous experiments.

The termination condition was set to 1000 generations,

and the average training error rate was calculated by performing the training 10 times. Experiments were performed separately for the purpose of comparison.

However, when the wavelet was not used, it was found that the error rate was above 15% more than with wavelength. Furthermore, Fig. 15 gave the graphical representation of error rate with and without wavelength. The error rate for ‘without wavelength’ was Table 7. Average error rate after training 1000 generations using training data for promising locations

No. Electrode Location 1 2 3 4 5 Avg. Error Rate

(%)

1 F7,F8,FC5,FC6 5.98 5.74 5.86 5.92 6.06 5.91

2 F3,F4,F7,F8,FC5,FC6 6.21 6.33 6.21 6.17 6.13 6.21

3 AF3,F7,F3,FC5 7.32 7.17 7.24 7.42 7.25 7.28

Table 8. Training average error rate according to whether wavelet is used or not

No. Usage Electrode Location Basis Avg. Error Rate (%)

1

With wavelength

F7,F8,FC5,FC6

D3 6.23

2 D4 5.91

3 D5 6.89

4

F3,F4,F7,F8,FC5,FC6

D3 7.29

5 D4 6.21

6 D5 7.43

7

AF3,F7,F3,FC5

D3 7.72

8 D4 7.28

9 D5 8.16

10

Without wavelength

F7,F8,FC5,FC6 21.93

11 F3,F4,F7,F8,FC5,FC6 22.78

12 AF3,F7,F3,FC5 24.10

Fig. 15. Comparison of training error rate with and without wavelength.

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plotted using the electrode location of experiment 10, 11 and 12 in Table 8. While the plot for ‘with wavelength’

was plotted using the Average Error Rate for D2, D3, D4 of each of the Electrode location of experiment 1 to 9.

4.5. Comparison of Training Results According to the Type of Membership Function

This experiment was performed to investigate whether the asymmetric Gaussian function is more suitable as a membership function than the symmetric function in the fuzzy system. Therefore, we compared the error rates by using symmetric Gaussian functions and asymmetric Gaussian functions as membership functions. In the case of chromosomes, symmetric Gaussian functions have the same left and right standard deviation. The termination condition was set to 1000 generations, and the training average error rate was confirmed by performing the training 10 times.

As can be seen in Table 9, the difference between symmetric and asymmetric Gaussian membership func-

tions is less than 2%. However, we can see that the asymmetric Gaussian function gives good results.

4.6. Comparison of Training Results according to Algorithm

In this experiment, we compared the backpropagation (BP) algorithm and the proposed method in updating the weight values. The input data used the {F7, F8, FC5, FC6} positions which showed the best average error rate in the previous experiments. We used the wavelet based features of the data extracted from EEG in both methods. In our proposed method, 50,000 generations were set as end conditions. We used five fuzzy rules and used the same number of hidden layer nodes.

In the neural network, 50,000 iterative training was also done.

The results of comparing the two approaches are shown in Table 10. The lowest error rate was 5.87%

with the proposed approach while the BP algorithm was 6.21%. The difference between the two training algorithms is not very large. However, it can be seen that the genetic algorithm converges faster than the BP algorithm^[26].

4.7. The Hyper Parameters of Asymmetric Gaussian Membership Function after Training and Experiment Results

In this experiment, parameter values for the membership function are obtained using the optimal electrode position. The Vs are the center value in each cluster. As previously established, the optimal number of clusters Table 9. The average error rate of training based on

Gaussian membership function

No. Electrode Location

Avg. Error Rate (%) Asymmetry

Gaussian Function

Symmetry Gaussian Function

1 F7,F8,FC5,FC6 5.91 7.65

2 F3,F4,F7,F8,FC5,FC6 6.21 8.33

3 AF3,F7,F3,FC5 7.28 8.67

Table 10. Comparison of results with two approaches

Generation Genetic Algorithm Backpropagation

1 2 3 4 5 1 2 3 4 5

30000 5.90 6.01 6.02 5.96 5.87 6.97 6.73 6.68 6.54 6.49

40000 5.90 6.01 6.02 5.95 5.87 6.53 6.55 6.52 6.32 6.34

50000 5.90 6.01 6.02 5.95 5.87 6.32 6.32 6.33 6.21 6.37

Table 11. Gaussian function mean values according to optimal cluster numbers

V1 V2 V3 V4

C1 4451.25 4368.86 4346.56 4594.4

C2 4545.6 4324.27 4335.07 4468.91

C3 4266.43 4525.16 4350.48 4745.49

C4 4578.63 4455.61 4782.53 4156.12

C5 4467.34 4416.89 4591.82 4241.61

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obtained by applying the clustering method is 5. Mean values and standard deviations are also obtained as a result of the same experiment. Since the mean values are representative values of each cluster, they are used as fixed values without updating through training. Table 11 shows the mean values of each cluster.

Table 12 shows the standard deviation values after training using the genetic algorithm. Since we used the asymmetric Gaussian function as the membership functions in the system, we managed the left and right standard deviation separately. The left and right values for

the standard deviation are derived after training. Before training, both the left and right have the same standard deviations. Fig. 16, shows the asymmetric membership function for each rule. From the graph, the asymmetric nature is clearly shown.

Using the optimal electrode location, Table 13 shows the results obtained by applying all the values found through the experiment to the system. Experiments were performed five times using training data and test data to obtain recognition results. The recognition results using the training data showed an average of Fig. 16. The standard deviations of the left and right of each fuzzy set relative to the fixed mean value.

Table 12. The standard deviation values of the asymmetric Gaussian function after training

Sl1 Sr1 Sl2 Sr2 Sl3 Sr3 Sl4 Sr4

C1 29.51 1.578 36.408 22.134 35.76 27.532 49.566 40.618

C2 2.0839 30.75 33.18 26.956 36.133 9.6833 14.926 43.417

C3 10.920 58.187 54.890 57.200 60.874 34.513 42.983 15.171

C4 35.670 19.477 73.54 46.657 53.628 7.9880 50.302 18.416

C5 12.574 34.871 5.5974 29.45 10.868 2.0001 2.7308 12.387

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94.2%. According to the results of five experiments, the recognition rate was evenly over 93% overall. Experi- mental results using the test data showed an average accuracy of 92.3%.

5. Conclusion

In this study, we used the EEG data from the human brain in order to grasp the intention of the user. The features of data were extracted using wavelets. Experimen- tal results show that extracted features using wavelet gives a better result. We applied the proposed system to various positions of EEG through many experiments.

In order to achieve optimum performance in the system, we have created a new model that combines neural network, fuzzy, and genetic algorithm. The structure of the general system has taken the form of neural networks for learning. A decision system that grasps user intention uses a fuzzy system structure. The Gaussian function is used as the membership function in the fuzzy system. In addition, asymmetric Gaussian functions were used. We introduced the genetic algorithm in the learning process of the parameters.

As a result of various experiments, it was concluded that the number of optimal fuzzy rules in this system is 5, which is the same in the experiment. The parameters related to the fuzzy system are learned and optimized through the learning process. The best results with 5.91% error rate were obtained when the training data were extracted from the electrode positions {F7, F8, FC5, FC6}. Likewise, the test data showed the smallest error rate as 6.31%. It was found that the data of the electrode position should be used for the user intention of the study. Of course, it may be different depending on the system, but in this study, backpropagation, which is a representative neural network structure, gave good results when compared.

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foun- dation of Korea(NRF) funded by the Ministry of Edu- cation (2017R1D1A1B03035988).

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