nils-herrmann/clean_scirepeval huggingface dataset:
Quantification of pain degree by frequency features of single-chanelled EEG

The final aim of this paper is to classify pain degree by only using Electroencephalogram (EEG) measured with single-channel. In clinical care, pain degree is needed for choosing and evaluating treatments, and it is important for clinicians to quantify pain degree as objectively as possible. Pain rating scales such as the Visual Analogue Scales (VAS) are usually used to quantify pain degree, which is only capable of subjective values due to self-report. From that, a method to quantify pain degree objectively has great importance. In this paper, we propose the possibility of quantifying pain degree by only using EEG measured with single-channel. 28 Subjects' EEG is measured while in 2 states; pain-free (VAS score of 0) and pain (VAS score of 3-4). By extracting frequency features from the measured EEG, and classifying using Support Vector Machine (SVM), the subjects in 2 states were classified with the accuracy of 100%. The results show reliability and validity of classifying pain degree using EEG measured with single-channel.
A design of the EEG feature detection and condition classification

In this paper, we classify the human conditions (before and after meal, before and after smoking) and extract the frequency feature of conditions by using the electroencephalograms (EEG). First, we measure the EEG data. Then, we classify the conditions by using the principal component analysis (PCA). Moreover, the EEG data is reconstructed by using the questionnaires and the result of classification. From the result, we consider ideal circumstance for the EEG measurement. Finally, the EEG data is decompressed to consider the EEG features of conditions. Then, in order to show the effectiveness of the proposed method, computer simulations are done.
A design of face detection system using evolutionary computation

We propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there are the same color as the skin color in scenes, the domain which is accepted as not only the skin color but any other color can be searched. However, first, the lip are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish the skin color from the others. The proposed method can obtain relatively high recognition accuracy, since it have the double recognition structure of LDNN and SDNN. Furthermore, in order to reduce the amount of calculation and to discriminate a skin color and the other colors, GA can search to minimize the number of necessary data. Then a storage capacity and the amount of operations can be cut down by using GA and LDNN and SDNN are trained by using the reduced data. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First, 100 lip color, 100 skin color and 100 background pictures, which are changed into 10/spl times/10 pixels, are prepared for training. The validity was verified by testing images containing several faces.
Effects of music on image impression and relationship between impression and physical feature

Auditory information plays an integral role in AV media because even identical images are perceived differently when they are matched with different music. We now present a few studies in which the changes in subjective impressions of when visual information is matched with auditory information were analyzed on the basis of the physical features of the perceived items. The purpose of this study is to investigate the effects of music on image impressions in terms of the physical features of images or music. In this paper, we first elucidate the changes in subjective impressions when the image is presented by itself and when it is presented with music. Secondly, to clarify the relationship between the impression and physical features, we compare their respective physical features, which include color information, structural information, and frequency characteristics with subjective impressions. As a result, the color information of an image containing green or saturation colors and the power of the music were strongly correlated with adjectives expressing activity. Moreover, the entropy of saturation correlated with words expressing spatial extent.
Strategy and verification to visualize experiential values in skill education service: Case study to nursing operation on Bed

Education service is human activity where a coach of a service provider gives a learner of a service receiver the value of knowledge and skill. In several fields where pedagogies and educational contents are well systemized, the service productivity has been enhanced in point of view of time and cost thanks to a classroom-based teaching and an e-learning approach. On the other hand in case of a skill education that is usually associated with body motion, above-mentioned orthodox educational styles are not effective by the following reasons: 1) the skilled motion itself is ambiguous and an extraction of the explicit knowledge is difficult, 2) for this reason it is difficult to communicate between the coacher and learner about the skill, and 3) customer royalty cannot be increased well since their trust relationship and customer satisfaction are not promoted. Therefore to improve this situation, the present authors first simplified ‘value of experience’ in both sides of coacher and learner into a ‘satisfying sense,’ and then try to evaluate the sense through applicable example. In this paper, the concept and analysis procedure are introduced with a practical example of caregiver's skilled motion based on "No Lifting Policy".
Construction of comfort evaluation system for streetscape improvement using electroencephalogram

In this paper, we propose a time-series comfort evaluation system for streetscape by using electroencephalogram (EEG). Since streetscapes have an impact on people's psychological condition on a daily basis, a streetscape evaluation system is required for streetscape improvement. In conventional ways, questionnaires have been mainly used to evaluate characteristics of the streetscapes. However, they have two pernicious defects. The first is that people usually forget their feelings before answering questionnaires. The second is that it is difficult to detect the condition of environmental elements because streetscapes change constantly. To detect environmental elements which affect people, we tried to evaluate comfort in streetscape using EEG in addition to questionnaires. In the experiments, we measured EEG signals when subjects walk and view streetscapes of Japanese city. We classified EEG signals into two classes: “comfort” and “discomfort”. Through the experiments across twenty one subjects, classification accuracy was achieved 76.1% using non-linear SVM. The results showed that a time-series EEG system, outputs discrete value of comfort in viewing streetscape, was constructed. We conducted additional experiment to verify the effectiveness of the system. For the experiments, we could detect comfort and discomfort elements that subjects really feel. We confirmed that the method using questionnaires and EEG is effective to evaluate comfort that people feel in streetscapes.
Classification and analysis of 2-color impression focusing on individual differences

It has often been pointed out that each color has a particular impression and that there are certain similarities and individual differences in the relationships between a color and its impression. We now present a few examples of individual studies that have focused on color impressions. In this paper, in order to examine individual characteristics of color impression, we propose a method that combines GA, PCA and CA. GA is used to select suitable parameters from color attributes -namely, RGB, HSI, and L⋆a⋆b⋆. PCA is used to extract characteristics data from the selected parameters. Furthermore, CA is used to classify the data set into groups. In order to demonstrate the effectiveness of this proposed method, computer simulations are performed and the results are analyzed using data gathered from 6 individuals. As a result, characteristics data that were obtained by using GA and PCA differed for each subject. Moreover, classification accuracy, which is the percentage of data classified correctly among all data, was more than 80% for all subjects. It is considered that the proposed method could be one of the effective methods for analyzing the individual characteristics of color impressions.
A robust gesture recognition based on depth data

In this paper, we propose a novel method for gesture recognition using depth data captured by Microsoft Kinect sensor. Conventionally, the features which have been used for gesture recognition are divided into two parts, hand shape and arm movement. In conventional methods, only two-dimensional hand features are used because human's hand consists of the multiple joint structure. Furthermore, conventional arm movement feature are influenced by environmental changing, such as individual differences in body size, camera position and so on. Therefore, to assist in the recognition, a method of feature extraction is proposed, which involves the hand shape with 3D feature and the arm movement with angle between joints of body. In order to show effectiveness of the proposed method, performance for gesture recognition is compared with conventional methods using Japanese language.
An Important Facial Parts Analysis for Evaluating the Impression - Difference by Gender

The concern for image processing has increased very much, in recent year. However, the evaluation of facial impression is not treated in this area that we are naturally doing in our daily life to see an ones face. Wide ranges of applications are released from the achievement of the impression automatic evaluation. Then we analyze important facial parts for impression analysis, in order to achieve automatic evaluation of facial impression. In order to analyze the important facial parts, we performed computer simulation. As the result of this simulation, difference of important areas by gender that relate impression words were detected.
Vowel recognition method by using features included in amplitude for mobile device

We propose a method of individual adaptive vowel recognition system, which uses features extracted from an amplitude waveform of vowel. We think that a simple vowel recognition system can be constructed by using a shape feature of waveform without converting a frequency. Then, we extract the amplitude features and divide five kinds of vowels into each combination of two kinds of vowels. The suitable amplitude features for vowel identification are selected for each vowel combination. A distribution of the suitable amplitude features is indicated by using discrete Voronoi diagram and sphere of influence of the amplitude feature is decided. A borderline between spheres of influence is identified to a function of vowel identification by using the least squares method. Finally, in order to show the effectiveness of the proposed method, computer simulations of vowel recognition are carried out by using the functions of vowel identification.
A design of the object detection system using the RGA

License plate recognition is very important is an automobile society. However, it is very difficult to do it, because the background and body color of cars are sometimes similar to that of the license plate. Furthermore, the detection of cars moving at a very high speed is difficult. In this paper, we propose a new robust thresholds-determination method in various backgrounds by using the real coded genetic algorithm (RGA). By using RGA, the most likely plate colors are decided under various light conditions. First, the average brightness Y values of images are calculated. Next, relationship between the Y value and the most likely plate color thresholds (upper and lower bounds) are obtained by GA to estimate threshold equations by using recursive least squares (RLS) algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images. The resulting rate of detection is 85.0%.
Eye blink detection using monocular system and its applications

In this paper, we propose a practical method for eye blink detection using a monocular system. Eye blink detection is an important technology in many situations such as facial action analysis and signal processing. However, automatic eye blink detection is quite difficult since the eye blink is occurred fast. Fast head tracking and eye detection methods are therefore required in real environment. The proposed method is based on the facial feature point tracking algorithm by using particle filtering. The proposed method works at over 60fps and is able to detect accurate blink motion in real time although our system require only one USB camera. In order to confirm the effectiveness, we show some of applications using our blink detection system.
A design of the preference acquisition detection system using the EEG

Estimation of emotional states has been multi-disciplinary research interests. Among them, although there are many ways of the estimation such as subjective evaluations and behavioral taxonomy, direct evaluation from the human brain is more reliable. Especially, electroencephalographic (EEG) signal analysis is widely used because of its simplicity and convenience. In our research, emotional states are investigated with a simple electroencephalography which has only one electrode. This device is lighter and cheaper than existing devices. However, its feasibility has yet been proven.
Detecting method of music to match the user’s mood in prefrontal cortex EEG activity using the GA

In this paper, we propose a method for detecting the mood much music for prefrontal cortex electroencephalogram (EEG) activity. The analyzed EEG frequencies contain significant and immaterial information components. We focused on the combinations of the significant frequency. These frequency combinations are thought to express personal features of EEG activity. In the proposed method, we calculate the spectrum of these frequency combinations rates that does not include the noise frequency components and evaluates whether the music matches the user's mood through a simple threshold processing. Then, a genetic algorithm (GA) is used to specify the frequency of personal features on the EEG. The threshold vale used the threshold processing is the average value of the spectrum rates specified EEG frequency combinations. Finally, the performance of the proposed method is evaluated using real EEG data.
A new rule generation method from neural networks formed using a genetic algorithm with virus infection

In this paper a new rule generation method from neural networks is presented. A neural network is formed using a genetic algorithm (GA) with virus infection and deterministic mutation to represent regularities in training data. This method utilizes a modular structure in GA. Each module learns a different neural network architecture, such as sigmoid and a high order neural networks. Those information is communicated to the other modules by the virus infection. The results of computer simulations show that this approach can generate obvious network structures and lead to simple rules.
Automatic detection of left ventricular asynergy by fuzzy reasoning

Recently, as sophisticated medical instruments have been developed, the internal state of a human body has become well-known. However, a doctor's burden becomes heavier because the number of images which are taken per person with medical instruments drastically increases. Therefore, the development of automatic diagnostic imaging systems is needed. Incidentally, as Japanese daily life is Americanized, heart diseases, such as angina and myocardial infarction, are increasing. We need to observe consecutive cardiac muscle motion to detect their diseases. The left ventricular axis and the contact points in the heart region are defined, and then cardiac muscle momentum is extracted. We discriminate an abnormal case and a normal case by using a neural network and fuzzy reasoning to confirm the effectiveness of our approach. Finally, in order to show the effectiveness of the proposed method, we show simulation examples using real images.
Sound quality indicating system using EEG and GMDH-type neural network

In this paper, we propose a sound quality evaluation system using electroencephalogram (EEG) and group method of data handling (GMDH) type neural network. Recently, EEG is used in various applications, and we focus on sound quality evaluation using EEG. We prepared EEG samples to train a GMDH-type neural network to recognise 3 typical types of sound which was used to create the training data. The results showed that using GMDH-type neural network improved recognition rate compared to the other method. Additionally, we repeated simulations by using different parameter of GMDH-type neural network, and the open test results showed the recognition rate variations in different parameter values.
Face detection and emotional extraction system using double structure neural networks

We propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there exists the same color as the skin color in scenes, the domain which is accepted as not only the skin color but any other color can be searched. However, first, the lips are detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish skin color from the other colors. The proposed method can obtain relatively high recognition accuracy, since it has the double recognition structure of LDNN and SDNN. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First, 100 lip color, 100 skin color and 100 background pictures, which are changed into 10/spl times/10 pixels, are prepared for training. The validity was verified by testing images containing several faces.
Motion artifact removal from photoplethysmography based on weighted band-pass filter

This paper presents the method of reducing motion artifact (MA) from photoplethysmography (PPG) measured from ears. It is a challenging problem to accurately calculate pulse rate and peak to peak interval (PPI) from MA-contaminated PPG. We propose a two-stage MA reduction filter for removing MA in PPG. Two-stage MA reduction filter is consisted of weighted band-pass (WBP) filter and adaptive filter. WBP filter is used to suppress MA in PPG. Then, the output signal of WBP filter is enhanced by adaptive filter. The MA reduction performance of the proposed method was evaluated in the computer simulation. The simulation results showed that the proposed method could suppress MA in PPG.
Quantification of pain degree using EEG with simple device

The final aim of this paper is to quantify pain degree by only using Electroencephalogram (EEG) measured with simple device. Pain degree dominates the choice and assessment of a treatment in clinical care. It is conventionally quantified using pain rating scales, although they are not capable of attaining objective values. Therefore, a method which can quantify pain degree objectively has great importance. In this paper, the possibility of quantifying pain degree by only using EEG measured with simple device is proposed. The proposed method is in an easy manner considering its time consummation and procedures. Significant difference in EEG features between pain-free, painful, and psychologically painful state were confirmed as the results of the experiment, showing the possibility of attaining objective pain degree by using EEG measured with simple device.
EEG frequency analysis for dozing detection system

It is important to estimate the driver dozing practically. In the conventional study, the electroencephalogram (EEG) has been a promising indicator to driver dozing. Furthermore, it is known that frequency of the EEG is highly related to the sleep and the wake conditions. Therefore, we extract frequency components of the EEG from the whole cerebral cortex, by using the rhythmic component extraction (RCE), proposed by Tanaka et al. RCE extracts a component by combining multi-channel signals with weights that are optimally sought for such that the extracted component maximally contains the power in the frequency range of interest and suppresses that in unnecessary frequencies. As a result, we confirmed the interested frequency power is emphasized. These results imply that this method is available for analyzing the sleeping.
Ship detection based on spatio-temporal features

The paper proposes the ship detection method based on Spatio-temporal Histograms of Oriented Gradients (STHOG) feature and Support Vector Machine (SVM). STHOG feature, which is the extension version of HOG feature, enables extract spatial and temporal features of an object. The ship detector based on HOG feature can wrongly detect the similar shape objects with ships. On the other hand, the ship detector based on STHOG feature can identify them successfully by utilizing temporal feature of an object. To extract temporal feature of an object, image registration is implemented and an image displacement by camera motion is corrected. Due to high dimensionality of STHOG feature, it requires high computational cost to scan entire image and find ship regions. Principal Component Analysis (PCA) is applied to STHOG feature to compress the dimension. In the computer simulations, the ship detection performance of the proposed method was evaluated. From the simulation results, our proposed method exhibited better results than ship detector based on PCA+HOG feature.
Scene image analysis by using the sandglass-type neural network with a factor analysis

It is difficult to obtain images we want on the Web due to enormous data that exist in the Web. A present image detection systems are keyword detection which is added to the name of keyword for images. Therefore, it is very important and difficult to add the keyword for images. In this paper, keywords in the image are analysed by using the factor analysis and the sandglass-type neural network (SNN) for image searching. As images preprocessing, objective images are segmented by the maximin-distance algorithm. Small regions are integrated into a near region. Thus, objective images are segmented into some region. After this images preprocessing, keywords in images are analyzed by using factor analysis and a sandglass-type neural network (SNN) for image searching in this paper. Images data are compressed to a 2-dimensional space by using these two methods. This 2-dimensional data space is presented by a graph. Thus, keywords are analyzed in detail.
Genetic lips extraction method for varying shape

In this paper, a lips extraction method that can extract lips region from varying lips shape at the moment of speech by using only one template image is described. The method that is proposed in this paper, has invariance for an open and closed mouth, showing or not showing any teeth, and has high speed and high extraction accuracy in consideration for characteristics of the lips by using the genetic method. This method uses the template matching based on the genetic algorithm. Furthermore, the colour of lips and characteristics of the lips shape variances at the moment of speech in this system are utilized. The effectiveness of this method is demonstrated with only one template for each person being tested and a search object, that is, the varying lip shapes at the moment of speech of vowels by means of computer simulations. These computer simulations indicate that this method can extract the varying lips shape at the moment of speech by using only one template. Moreover, in the extraction processing of every vowel, a high speed and high extraction accuracy can be obtained.