nils-herrmann/clean_scirepeval huggingface dataset:
An extraction method of liver tumors by using genetic algorithms and neural networks

Recently, internal human organ disorders that medical image analysis can be used to detect is being actively researched. The research have however, concentrated on the extraction of pulmonary tumors. There is therefore, little research being done on the extraction of liver tumors. This is because there is no difference between concentrated values of a healthy part and one with a tumor in liver CT images. In this paper, the extraction method of such liver tumors is proposed. Furthermore, in order to demonstrate the effectiveness of the proposed scheme, we show a simulation example, using real CT image data.
Invisible calibration pattern based on human visual perception

In the print-type steganographk system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on human visual perception. We embed the calibration pattern in an original image by adding high frequency component to blue intensity in a limited region. Moreover, the proposed calibration pattern protects page layout and artwork.
Out-of-focus blur image restoration using the Akamatsu transform

In this paper, a new image restoration approach using the Akamatsu transform is presented. In the method, by using repeated simple calculations based on the Akamatsu transform, an out-of-focus blurred image can be restored and sharpened. Out of focus is a major problem bothering many people in photography, especially the amateurs. There exits some solution to this problem on both the hardware and software sides. However, none offers a perfect solution. The Akamatsu transform can easily be embedded into hardware to offer faster processing due to its simplicity. Although, initially proposed for speech processing, this paper show the effectiveness of the transform in image processing. The Akamatsu transform is a combination of integral and differential transforms. The algorithm can be effective tools for image restoration in realtime image processing.
Fast Statistical Learning with Kernel-Based Simple-FDA

In this paper, new statistical learning algorithms with kernel function are presented. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. However, the Fisher linear discriminant analysis (FLDA) has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to simple-PCA and does not need matrix operation. In this paper, new statistical kernel based learning algorithms are presented. They are extended versions of simple-PCA and simple-FLDA to nonlinear space using the kernel function. Their preliminary simulation results are given for a simple face recognition problem.
Detection of KANSEI value trends using the electroencephalogram during watching memorable TV commercials

In this paper, we aimed to get the trends of the KANSEI, which is alike “how to feel”, values of the memorable TV commercials (CMs) using the electroencephalogram (EEG) while subjects watch TV CMs. KANSEI is Japanese word because of studying at first in Japan. The questionnaire has been used as conventional evaluation method of TV CMs. This method is subjective evaluation, so it is difficult to know the details of situations that affect memory because it cannot get instantaneous evaluation. To solve this problem, we did memory follow-up questionnaire surveys and measured the EEG when watching memorable TV CMs that is objective evaluation using the bio signals. We got KANSEI values from EEG data using “KANSEI Analyzer”. A KANSEI Analyzer is an application which detects subject's degrees of five emotional states: “Like”, “Interest”, “Concentration”, “Calmness”, and “Stress”, by original system constructed by Dentsu ScienceJam Inc‥ The degree of each emotional state is calculated per second, which is represented in percentage (0%: low degree - 100%: high degree). We did carry out two experiments and compared KANSEI values. We performs correlation analysis, and get trends of KANSEI values in watching memorable TV CMs. As a result, we got the trends of KANSEI values when watching memorable TV CMs that “Concentration” and “Stress” values 3∼10% decreased toward end of TV CMs, and went up in last few seconds. Also, “Interest” value increased. Accordingly, we were able to get the trends of the KANSEI values of the memorable TV CMs using the EEG when watching TV CMs.
A Method for Evaluating the Degree of Human's Preference Based on EEG Analysis

We always employ a subjective evaluation for recognizing user’s preference. However, it depends on the day or the person. It is difficult to evaluate the human’s preference objectively, but it is needed for developing in the factory. By the way, sense of touch is important factor for human to decide what they like. In this paper, we propose a method for extracting an information on the sense of touch based on electroencephalogram (EEG) analysis. We analyze the EEG signals while we touch objects. We often use the frequency analysis for data analysis of the EEG. The frequency analysis is used in much work of EEG. There seems to be frequency bands reflected in the preference significantly. Therefore, we apply particle swarm optimization (PSO) for selection of the significant component. For separating, support vector machine (SVM) is used. Experimental result shows that the error rates of the proposed method indicate lower value. Therefore, proposed method has the capability of separating human’s preference.
Rhythmic component extraction considering phase alignment and the application to motor imagery-based brain computer interfacing

We propose a novel method for extracting a rhythmically oscillating signal from EEG recordings including multiple source signals which have similar frequencies. The main application of this method is brain computer interfaces (BCI), which use rhythmically oscillating signals such as alpha, mu, and beta waves, as feature signals. It is difficult to separate those components and/or extract an command-related component when these feature signals span the same frequency band. The main idea is to assume that signals generated in different brain parts have different phases, even though they have the same frequency. This hypothesis is effectively incorporated with the previously proposed rhythmic component extraction (RCE) method, which successfully extracts a signal oscillating at a certain frequency from multi-channel sensor signals in the BCI application. The signal model is firstly given and then this novel extraction method is formulated as an optimization problem. We apply the proposed method for the classification of multi-channel EEG signals between imaginary left/right hand movement. Our experiment suggests that the proposed method is effective in feature extraction for motor-imagery based BCI.
Study on relationship between personality and individual characteristic of EEG for personalized BCI

It is an important issue that the relationship between personality and individual characteristic of an electroencephalogram (EEG) for using a personalized brain computer interface on daily basis. In this paper, we introduce a method for discussing the relationship between personality based on egogram scores and the results of the EEG pattern detection. The egogram based on psychological testing is used for analyzing human personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified/detected using the knearest neighbor method. Finally, we discuss the relationship between human personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection ratio of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.
A new genetic approach to universal rule generation from trained neural networks

A new rule generation method from neural networks is presented. A neural network (NN) 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 higher order neural networks. Those chromosome information is communicated to the other modules by the virus infection. The higher order units are connected to an output unit or hidden units. By using these architectures, rules can be extracted. The results of computer simulations show that this approach can generate obvious network architectures and as a result simple rules.
License plate detection system by using threshold function and improved template matching method

License plate recognition is very important in an automobile society. However, it is very difficult to do it, because a background and a car surface color can be similar to that of the license plate. Furthermore, detection of cars moving at a very high-speed is difficult to be done. We propose a new method to extract a car license plate automatically by using a genetic algorithm (GA). By using GA, 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 the RLS algorithm. Finally, in order to show the effectiveness of the proposed method, we show simulation examples by using real images.
Bimodal BCI Using Simultaneously NIRS and EEG

Although noninvasive brain-computer interfaces (BCI) based on electroencephalographic (EEG) signals have been studied increasingly over the recent decades, their performance is still limited in two important aspects. First, the difficulty of performing a reliable detection of BCI commands increases when EEG epoch length decreases, which makes high information transfer rates difficult to achieve. Second, the BCI system often misclassifies the EEG signals as commands, although the subject is not performing any task. In order to circumvent these limitations, the hemodynamic fluctuations in the brain during stimulation with steady-state visual evoked potentials (SSVEP) were measured using near-infrared spectroscopy (NIRS) simultaneously with EEG. BCI commands were estimated based on responses to a flickering checkerboard (ON-period). Furthermore, an “idle” command was generated from the signal recorded by the NIRS system when the checkerboard was not flickering (OFF-period). The joint use of EEG and NIRS was shown to improve the SSVEP classification. For 13 subjects, the relative improvement in error rates obtained by using the NIRS signal, for nine classes including the “idle” mode, ranged from 85% to 53 %, when the epoch length increase from 3 to 12 s. These results were obtained from only one EEG and one NIRS channel. The proposed bimodal NIRS-EEG approach, including detection of the idle mode, may make current BCI systems faster and more reliable.
Recognition of Wrist Motion Pattern by EMG

Recently, studies of artificial arms and pointing devices using ElectroMyoGram (EMG) have been actively done. However, the individual variation of EMG is large, and its repeatability is low. Furthermore, EMG is usually measured from a part with comparatively big muscular fibers such as arms and shoulders. Therefore, if we can recognize wrist operations using EMG which was measured from the wrist, the range of application can extend furthermore. In this study, we aim toward the development of a device of wristwatch type that consolidates operational interface of various equipments. In particular, as an early stage, we propose a wrist motion recognition system. First, we execute the Fourier transform to the signal for feature extraction. Next, we experiment it by using neural networks after the dimensional reduction by using simple-PCA and simple-FLDA to reduce the number of inputs to NN. It was confirmed that the present approach was one of the techniques which were effective in the wrist recognition experiment
A neural network approach to color image classification

The paper presents a method for image classification by neural networks which uses characteristic data extracted from images. In order to extract characteristic data, image pixels are divided by a clustering method on HSI 3-dimensional-color space and processed by labeling to select domains. The information extracted from the domains is characteristic data (color information, position information and area information) of the image. Another characteristic data, which is extracted by Hough transform, is added to the feature and a comparative experiment is conducted. Finally the validity of this technique is verified by means of computer simulation.
A basic method for classifying humans based on an EEG analysis

This paper introduces a method for classifying humans by analyzing prefrontal cortex electroencephalogram (EEG) activity to extract and confirm distinct response features on listening to music that the user feels matches his/her mood, does not match his/her mood, or otherwise. The proposed method constitutes analyzing EEG signals obtained from monitoring human response features and classifying the human subjects according to the different frequency bands of the power spectrum of the EEG signal. The performance of the proposed method is evaluated using real EEG data. We confirm that we can classify humans into at least three groups.
ICA-based positive semidefinite matrix templates for eye-blink artifact removal from EEG signal with single-electrode

Complete artifact removal of eye-blink for electroencephalographic (EEG) signal is generally-regarded as important process in EEG signal analysis. No numerical approach of eye-blink artifact removal for single-channel EEG signal in time domain is developed. In this paper, we propose a time domain eye-blink artifact removal method for single-channel EEG signal using independent component analysis (ICA)-based templates. ICA and positive semidefinite tensor factorization (PSDTF) are employed in our proposed method. As a result, we obtain high signal-to-noise ratio (15.03dB) and low mean square error (25.76). Moreover, the optimal number of iterations on multiplicative update rules in PSDTF is about 20 to 30. A useful time domain eye-blink artifact removal method for single-channel EEG signal was proposed by using ICA-based PSD matrix templates. Improvement of eye-blink artifact removal accuracy and reduction of computational cost are our main future works.
Age and gender estimation by using facial image

In this paper, we propose age and gender estimation system by vairous features. Age and gender has a lot of characteristics. These characteristics are one of the difficult cognitive process in human interaction. If we can extract the important feature of this cognitive process, it is considered that the age and gender estimation by the machine becomes possible. Therefore, we propose a method of age and gender feature extraction and estimation using the face image. In this paper, the age of the face means apparent-age that is based on the human perception of age. Moreover, person's aging and gender difference appear in the faces. For example, the pigmented spot, the wrinkle, sagging skin, shape, color of skin, and so on. Thus, we extract these several features for age and gender estimation. Furthermore, we estimate a continuous age and gender using a neural network (NN).
True smile recognition system using neural networks

Recently, research about man-machine interfaces has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigen-face method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos /spl theta/ is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using neural networks (NN), the value of cos /spl theta/ between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done.
Integrating interference frequency components elicited by monitor refresh rate to enhance frequency detection of SSVEPs

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize a direct communication between the human brain and the outside environment in real-life situations. Recently, we proposed a stimulation approximation approach to increase the number of visual stimuli that can be realized on a computer monitor. In addition to the fundamental and harmonic frequencies, the refresh rate-based stimulation approach also elicits SSVEPs at other frequencies that are termed interference frequencies, which are derived from the interaction between the stimulation frequency and the refresh rate. This study aims to investigate properties of the interference frequency components, and propose to integrate the interference frequency components to enhance frequency detection of SSVEPs. The results suggest that the proposed approach could substantially improve the frequency detection accuracy of SSVEPs.
A design of the preference acquisition detection system

Recently in the world, there are a lot of reports concerning the relation between the EEG and the preference in the analysis of the biosignal. In this paper, personal preference using olfactory stimulus and the EEG are taken out. We measure the EEG when smelling favorite odor and dislikeable odor. Then, the feature data of the EEG are extracted by the proposed method. Furthermore, in order to show the effectiveness, we demonstrate simulation examples. From the simulation result, it was confirmed that the proposed method works well.
A design of face detection system using the GA and the simple PCA

Recently in the world, many researches of individual recognition method using biometrics are widely done. Especially, individual recognition using faces are used because of needless for physical contact. When we specify with an individual, the information from a face is the most reliable. In this paper, we propose the new method of individual recognition using Simple PCA and NN. Individual recognition system needs to have not only ability to recognize the registrant correctly but also ability to reject the other person certainly. therefore, our objective is to reject certainly the other person. Furthermore, we analyzed and examined about the individual feature in a face using GA. Moreover, in order to show the effectiveness of the proposed method, we show computer simulations by using real image. From these results, we show the effectiveness.
Iterative PCA approach for blind restoration of single blurred image

This paper proposes a single-channel image blind restoration using iterative principal components analysis (PCA) to improve the quality of restoration. Previously proposed PCA approaches for blind restoration have a lot of problems. For example, the process of boosting high-frequency components would be improvable, no numerical evaluation has been performed, and etc. Generating an ensemble by means of Gaussian filter application, discussed in this paper, could improve to extract the high frequency components which had been lost. Furthermore, iterative PCA boosts the high frequency components. Our proposed method is applied to a restoration example of atmospheric turbulence-degraded imagery, and we verified to improve restoration quality in comparisons with conventional methods. For demonstrating comparative experiments, simulations have been conducted. From the results, we can confirm that the proposed method gives higher PSNR as well as SSIM than the conventional methods.
The EEG analysis method for obtaining the feeling

The electroencephalogram (EEG), the brain activity of neurons in the brain, is used to record brain activity for many purposes. It also changes depends on mental conditions because mental conditions are generated in the brain. In this study, we try to estimate the mental conditions by observing the EEG patterns. The EEG data caused by mental changes is obtained as giving the subjects animal assisted therapy. The method for estimation is AdaBoosting as the neural network. We set the input vector as EEG changes and output vector is set as mental changes. Subjects are 25 people: 19 males and 6 females (age: 21-24 years old). Previous study reveals the correlation between the EEG and the activeness, and between the EEG and the tiredness. Therefore, in this paper, we try to estimate these mental parameters by the EEG at the first step.
Visualization of happiness elicited taking a food using near infra-red spectroscopy

The purpose in this study is visualizing brain functional activities during taking a favorite food using Near Infra-Red Spectroscopy (NIRS). Many appointments and systems that aim to provide happiness for improving quality of life in the users have been constructed. The creations must integrate a common and plausible happiness indicator into their module; however, almost all of them do not have the indicator because constructing a common happiness indicator is very difficult. To solve this issue, we visualize happiness with hemal indices that would reflect well-known relation between happiness and brain functional activities. Before this study, we employed questionnaire to define the meaning of happiness. Thus, we measured hemal indices during taking a favorite (sweet/salty) food; people feel happiness in the state, and resting state by using NIRS. In the analysis part, first-order auto-regression and t-test are employed for modeling of hemodynamic changing and visualizing brain functional activities. As a result, 11 and 15 points on the occipital region show significant differences when the subjects take a sweet or salty food. The results would contribute to construct plausible happiness indicator.