Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks

Braincomputer interfaces (BCI) facilitate communication between the human brain and computational systems, additionally offering mechanisms for environmental control to enhance human life. The current study focused on the application of BCI for communication support, especially in detecting unspoken answers to questions. Utilizing a multistage neural network (MSNN) replete with convolutional and pooling layers, the proposed method comprises a threefold approach: electroencephalogram (EEG) measurements, EEG feature extraction, and answer classification. The EEG signals of the participants are captured as they mentally respond with yes or no to the posed questions. Feature extraction was achieved through an MSNN composed of three distinct convolutional neural network models. The first model discriminates between the EEG signals with and without discernible noise artifacts, whereas the subsequent two models are designated for feature extraction from EEG signals with or without such noise artifacts. Furthermore, a support vector machine is employed to classify the answers to the questions. The proposed method was validated via experiments using authentic EEG data. The mean and standard deviation values for sensitivity and precision of the proposed method were 99.6% and 0.2%, respectively. These findings demonstrate the viability of attaining high accuracy in a BCI by preliminarily segregating the EEG signals based on the presence or absence of artifact noise and underscore the stability of such classification. Thus, the proposed method manifests prospective advantages of separating EEG signals characterized by noise artifacts for enhanced BCI performance.


Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music

We propose a method to detect human wants by using an electroencephalogram (EEG) test and specifying brain activity sensing positions. EEG signals can be analyzed by using various techniques. Recently, convolutional neural networks (CNNs) have been employed to analyze EEG signals, and these analyses have produced excellent results. Therefore, this paper employs CNN to extract EEG features. Also, support vector machines (SVMs) have shown good results for EEG pattern classification. This paper employs SVMs to classify the human cognition into wants, not wants, and other feelings. In EEG measurements, the electrical activity of the brain is recorded using electrodes placed on the scalp. The sensing positions are related to the frontal cortex and/or temporal cortex activities although the mechanism to create wants is not clear. To specify the sensing positions and detect human wants, we conducted experiments using real EEG data. We confirmed that the mean and standard deviation values of the detection accuracy rate were 99.4% and 0.58%, respectively, when the target sensing positions were related to the frontal and temporal cortex activities. These results prove that both the frontal and temporal cortex activities are relevant for creating wants in the human brain, and that CNN and SVMare effective for the detection of human wants.


Individual Differences in Brain Activities When Human Wishes to Listen to Music Continuously Using Near-Infrared Spectroscopy

This paper introduces an individual difference in the activities of the prefrontal cortex when a person wants to listen to music using near-infrared spectroscopy. The individual differences are confirmed by visualizing the variation in oxygenated hemoglobin level. The sensing positions used to record the brain activities are around the prefrontal cortex. The existence of individual differences was verified by experiments. The experiment results show that active positions while feeling a wish to listen to music are different in each subject, and an oxygenated hemoglobin level is different in each subject compared to its value when a subject does not feel the wish to listen to music. The experiment results show that it is possible to detect a wish to listen to the music based on changes in the oxygenated hemoglobin level. Also, these results suggest that active positions are different in each subject because the sensitivities and how to feel on stimulus are different. Lastly, the results suggest that it is possible to express the individual differences as differences in active positions.

International Journal of Advanced Science and Technology


Japanese sign language classification based on gathered images and neural networks

This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words.

International Journal of Advances in Intelligent Informatics


Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion

With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security.As a security enhancement method, we proposed a method to carry out numeral identification and personal authentication using numerals written in the air with Leap motion sensor. In this paper, we also focus on proper handling of aerial input numerals to verify whether the numerals written in the air are helpful for authentication. We collect numerals 0 to 9 from five subjects, then apply three preprocessing to these data, learn and authenticate them by CNN (convolutional neural network) which is a method of machine learning. As a result of learning, an average authentication accuracy was 92.4%. This result suggests that numerals written in the air are possible to carry out personal authentication and it will be able to construct a better authentication system.

IoTの進歩により，すべてがネットワークに接続されるようになる．それは多くの利点をもたらすが，ネットワークを接続することによっていくつかのセキュリティリスクが発生する．このような問題を回避するには，現在よりもセキュリティを強化することが不可欠である．セキュリティの1つとして個人認証に焦点を当てており，セキュリティ強化の方法として，Leapモーションセンサーで空中に書かれた数字を使って数字の識別と個人認証を行う方法を提案した．本論文では，空中に入力された数字が認証に役立つかどうかを検証するために，空中入力数字の適切な処理にも焦点を当てている． 5人の被験者から0~9の数字を収集し，これらのデータに3つの前処理を適用して，機械学習の方法であるCNN(畳み込みニューラルネットワーク)で学習および認証している．学習の結果，平均認証精度は92.4%であった．この結果は，空中に書かれた数字が個人認証を実行することが可能であり，より良い認証システムを構築できることを示唆している．

Advances in Science, Technology and Engineering Systems


A Gathered Images Analysis Method to Evaluate Sound Sleep

This paper proposes a method to evaluate a sound sleep using an image gathering technique and itsanalysis techniques. The proposed method consists of three phases; gathered images generation, gathered images analysis and sound sleep evaluation. The gathered images designed to gather sleep postures and their changes are generated at 1 second, 10 seconds, 1 minute, 10 minutes, 1 hour intervals and all times, respectively. In the gathered image analysis, the gathered images are analyzed by calculating difference values among the gathered images of 10-minute and all times. Then, the sound sleep conditions are evaluated by visual inspection and analysis results. In order to show the effectiveness of the proposed method, we conduct experiments using real movies and their images. In experimental results, we confirm that there were sound sleep conditions, bad sleep conditions and borderline cases by checking subjective evaluation using questionnaire and generated gathered images visually. Moreover, we confirm that the calculated difference values among the gathered images of 10-minute and all times are different between sound sleep and other cases. Furthermore, the analyzed results show that the proposed method was successful in the sleep conditions classifications on four of five subjects. These results suggest that the gathered images analysis method is effective for evaluating whether sleep condition is sound sleep or not. In particular, it is important to calculate the difference values among the gathered images of 10-minute and all times to evaluate sleeping conditions.

Journal of the Institute of Industrial Applications Engineers


An Electroencephalogram Analysis Method to Detect Preference Patterns Using Gray Association Degrees and Support Vector Machines

This paper introduces an electroencephalogram (EEG) analysis method to detect preferences for particular sounds. Our study aims to create novel braincomputer interfaces (BMIs) to control human mental (NBMICM), which are used to detect human mental conditions i.e., preferences, thinking, and consciousness, choose stimuli to control these mental conditions, and evaluate these choices. It is important to detect the preferences on stimuli. If the stimuli related to the preference can be detected, the NBCIMC can provide stimuli to the user based on their emotions by detecting their favorite stimuli. The proposed method adopted EEG recording technique, extraction techniques of EEG features and detection methods of preferences. EEG recording employs a simple electroencephalograph, for which the measurement position is the left frontal lobe (Fp1) of the brain. We assume that the differences of the EEG activities on the patterns of preference are expressed in the association between the changes of the power spectra on each frequency band of the EEG. To calculate the association, we employ the gray theory model. The EEG feature is extracted by calculating the gray association degree, then, the preferences are detect using a support vector machine (SVM). Experiments are conducted to test the effectiveness of this method, which is validated by a mean accuracy rate >88% on the favorite sound detection. These results suggest that the detection of subjects favorite sounds becomes easy when the EEG signals are analyzed while the gray associate degrees are used as the EEG feature and the SVM is used as the classifier.

聴取音に対する嗜好を脳波による分類する手法を提案する．提案手法は，脳波計測部，特徴抽出部，嗜好検出部で構成される．脳波計測部では，乾式センサを有する簡易脳波計を用いて，音聴取時の脳波を計測する．計測箇所は左の額である．特徴抽出部では，周波数分析された脳波の周波数に対する時間変化の値を算出し，周波数の時間変化の関連性を灰色理論を用いて，灰色関連度を算出することで計算する．その計算結果を特徴量とみなす．嗜好検出部では，サポートベクターマシンを用いて，嗜好を検出する．提案手法の有効性を検証するために，実験を実施した結果，良好な結果を得るに至った．


A Specification Method of Character String Region in Augmented Reality

This paper proposes a method to enter characters and/or character string in an augmented reality usinga gesture motion. The proposed method detects the region of character string using the gesture motion. It consistsof five phases; template generation, skin color detection, hand region detection, gesture motion extraction anddesignation of character string region. The template image consists of two fingers because a gesture is to takehold the tips of the first and second fingers. In the skin color detection, we extract the skin color on the basis ofvalues in saturation by using threshold processing. The hand region is detected by calculating areas and detectingthe area with the maximum value as a hand. The gesture motion is extracted using template matching. In orderto show the effectiveness of the proposed method, we conduct experiments for character string specification.

拡張現実空間において，手指ジェスチャーを用いて，文字および文字列の領域を特定する手法を提案する．提案手法は，テンプレート操作，肌色検出，手領域検出，ジャスチャー認識，文字列領域特定，で構成される．テンプレートは親指と人差し指が閉じている状態を用いる．また，手指ジェスチャーでは，親指と人差し指を閉じるジェスチャーをスイッチの代替とし，文字および文字列を囲むスタート地点と終了地点を特定している．手指ジェスチャーを検出するために，肌色・手領域を特定した後に，テンプレートマッチングにより，閉じているかどうかの判断をする．提案手法の有効性を検証するために実験を実施した．


Modeling of Drivers Distraction State based on Body Information Analysis

International Journal on Advances in Life Sciences


Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

In this paper, we propose a method which can discriminate hand motions. We measured an electromyogram of wrist by using 8 dry type sensors. We focused on four motions, such as ``Rock-Scissors-Paper'' and ``Neutral''. ``Neutral'' is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove hum noise. Next, we combine values of sensors based on a gaussian function. In this gaussian function, variance and mean are 0.2 and 0, respectively. After that, we apply normalization by linear transformation to the values. Subsequently, we resize the values into range from -1 to 1. Finally, support vector machine (SVM) conducts learning and discrimination. We conducted experiments in three subjects. Discrimination accuracy of the proposed method for three subjects was 96.9%, 95.3%, 92.2%, respectively. It is clear that the gaussian function is robust to difference of sensor position because this function combines both adjacent channels of sensors.

本論文では，8個の乾式センサを手首周囲に設置して計測された筋電によるての 動作識別の方法を提案している．手の動作は日本のジャンケン(グー，チョキ，パー)とニュートラル動作である．提案方法では，筋電信号にFFTを適用し周波数成分に変換し，ハムノイズを除去している．その後，筋電センサの信号にチャネル間の平滑化を適用する．そして，信号値を-1∼+1に正規化し，サポートベクタマシンで学習識別を行った．識別率は，被験者3名に対し，96.9%，95,3%，92.2%であった．センサ間の平滑化により，センサ設置位置のずれに対して頑健であることが示された．

ECTI Transactions on Computer and Information Technology


A Consideration for Electroencephalogram Analysis using Self-Organizing Map Based on Learning Algorithm for Plural-Attribue Information

複属性データ対応型自己組織化マップを用いた脳波分析に関する一考察

This paper discusses a method to detect electroencephalogram (EEG) patterns using a self-organizing map(SOM) based on a learning algorithm for plural-attribute information (SOMPA). The input data for SOMPA has two attributes which are EEG feature and individual feature. We set the EEG feature to main feature and individual feature to sub-attribute information. The winning node in the learning algorithm of SOMPA is determined by using main feature and sub-attribute information. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are , and , respectively. The individual feature is analyzed though the ego analysis using psychological testing. In order to prove the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results show that the EEG pattern detection accuracy using SOMPA improves compared with the standard SOM.

サブ属性を考慮した改良型勝者ノード決定アルゴリズムを有する自己組織化マップを用いた脳はパターン検出法について考察する．改良型勝者ノー決定アルゴリズムでは，主属性のデータと副属性のデータを別々に使用し，それぞれ勝者ノード候補を決定する．それぞれの勝者ノード候補から任意の距離内にあるノード領域を選抜し，その領域が重なっている場合，重なった領域内において，最も主属性のデータと一致するノードを勝者ノードとする．重なりが無い場合，主属性の勝者ノード候補を勝者ノードと賭する．脳波を用いて嗜好を検出する問題に適用し，提案手法の有効性を検証した結果，良好な結果を得るに至った．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Method to Classify Matching Patterns between Music and Humans Mood Using EEG Analysis Technique Considering Personality

In this paper we introduce a method to classify matching patterns between music and human mood using an electroencephalogram (EEG) analysis technique and considering personality. We analyse the EEG of the left prefrontal cortex by single-point sensing. The EEG recording device uses dry-type sensors. The feature vector is created by connecting the personality quantification results and the EEG features. Egogramsthe Yatabe-Guilford personality inventory and a Kretschmer-type personality inventory are used to quantify personality. The EEG features are extracted using fast Fourier transform. Then, the matching patterns are classified using the k-nearest neighbour method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data.

ヒトのパーソナリティを考慮した脳波分析を利用して気分と一致した音楽を分類するための手法を紹介する．提案手法は左前頭極から得られる脳波を分析する．脳波計には乾式タイプのセンサを有する簡易脳波計を使用する．パーソナリティの分析には，エゴグラム，YG検定，KT検定を使用する．脳波分析は，周波数分析およびk近傍法を用いる．提案手法の有効性を検証するために，実験を実施した．

The Online Journal on Computer Science and Information Technology, OJCSIT


Development of Eye Mouse Using EOG signals and Learning Vector Quantization Method

Recognition of eye motions has attracted more and more attention of researchers all over the world in recent years. Compared with other body movements, eye motion is responsive and needs a low consumption of physical strength. In particular, for patients with severe physical disabilities, eye motion is the last spontaneous motion for them to make a respond. In order to provide an efficient means of communication for patients such as ALS (amyotrophic lateral sclerosis) who cannot move even their muscles except eye, in this paper we proposed a system that uses EOG signals and Learning Vector Quantization algorithm to recognize eye motions. According to recognition results, we use API (application programming interface) to control cursor movements. This system would be used as a means of communication to help ALS patients.

眼の動作識別は多くの興味を引きつけている．特に，身体の不自由な患者(ALS等の四肢麻痺など)にとって，眼の動作は最終段階まで利用・応答できる重要な要素である．それらの患者(眼以外を動かせない人)のために効率的な通信手段を提供するために，本論文では眼電に基づくシステムを提案している．本手法は，眼電位を学習ベクトル量子化により学習識別し，さらに筋電の識別ルールに基づいて高精度な認識を実現している．さらに，ウィンドウズ上で動作するシステムを構築し，実際に眼電位でマウスを操作できるシステムを実現した．このシステムはALS患者たちにとって，非常に重要な通信手段となる．

Journal of the Institute of Industrial Applications Engineers


Lost Property Detection by Template Matching using Genetic Algorithm and Random Search

In this paper, we propose an object search method which is adapted to transformation of an object to be searched to detect lost property. Object search is divided into two types; global and local searches. We used a template matching using Genetic Algorithm (GA) in the global search. Moreover we use a random search in the local search. According to experimental results, this system can detect rough position of the object to be searched. The search accuracy obtained using the present method is 83.6%, and that of a comparative experiment using only GA is 42.1%. We have verified that our proposed method is effective for lost property detection. In the future, we need to increase search accuracy to search objects more stably. In particular, we need to improve local search.

本論文では，監視カメラの画像を想定し自動で監視する方法を提案している．通常，監視カメラの映像での物体探索では様々なサイズと形状の変化がある．そのため，物体の変形に対応できる方法が必要である．本論文の方法では，遺伝的アルゴリズムを用いることにより，映像内に残された忘れ物を高速・高精度に検出する方法を提案している．特に，大域的探索を遺伝的アルゴリズムが担当し，局所的探索をランダム探索法で行う方法を提案している．さらに，複数の忘れ物を候補として検出する方法により，比較的高精度に忘れ物の検出が可能であることを示している．結果として本手法で，83.6%の探索精度を実現している．

Journal of the Institute of Industrial Applications Engineers


Unsupervised Segmentation of MR Images for Brain Dock Examinations

We propose an unsupervised segmentation method for magnetic resonance (MR) brain imaging by hybridizing the self-mapping characteristics of 1-D Self-Organizing Maps (SOMs) and by using incremental learning functions of fuzzy Adaptive Resonance Theory (ART). The proposed method requires no operator to specify the representative points. Nevertheless, it can segment the tissues (e.g., cerebrospinal fluid, gray matter, and white matter) necessary for brain atrophy diagnosis. To evaluate the effectiveness of the proposed method, we specifically examine Fuzzy C-means (FCM) and Expectation Maximization Gaussian Mixture (EM-GM) with prior setting of the cluster number, and Mean Shift (MS) without prior setting of the cluster number. These experiments on the two metrics confirmed that our method can achieve higher accuracy than these conventional methods.


Preference Analysis Method Applying Relationship between Electroencephalogram Activities and Egogram in Prefrontal Cortex Activities --- How to collaborate between engineering techniques and psychology

This paper introduces a method of preference analysis based on electroencephalogram (EEG) analysis of prefrontal cortex activity. The proposed method applies the relationship between EEG activity and the Egogram. The EEG senses a single point and records readings by means of a dry-type sensor and a small number of electrodes. The EEG analysis adapts the feature mining and the clustering on EEG patterns using a self-organizing map (SOM). EEG activity of the prefrontal cortex displays individual difference. To take the individual difference into account, we construct a feature vector for input modality of the SOM. The input vector for the SOM consists of the extracted EEG feature vector and a human character vector, which is the human character quantified through the ego analysis using psychological testing. In preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band: theta, low-beta, and high-beta. To prove the effectiveness of the proposed method, we perform experiments using real EEG data. These results show that the accuracy rate of the EEG pattern classification is higher than it was before the improvement of the input vector.

本論文では，前頭前野脳波分析技術を用いて嗜好を分析する手法を紹介する．脳波分析では，脳波の周波数特性を定量化する．脳波に含まれる個人差を緩和するために，エゴグラムを用いてヒトの性格を定量化する．これら脳波の特徴量とエゴグラムの得点を特徴ベクトルとし，嗜好パターン分類の問題適用することで，提案手法の有効性を検証する．パターン分類識別機には自己組織化マップを用いる．実験検証の結果，提案手法の有用性が確認できた．

International Journal of Advances in Psychology


Preference Classification Method Using EEG Analysis Based on Gray Theory and Personality Analysis

This paper introduces a method to classify the preference patterns of sounds on the basis of an electroencephalogram (EEG) analysis and a personality analysis. We analyze the EEG of the left prefrontal cortex by single-point sensing. For EEG recording, a dry-type sensor and few electrodes were used. The proposed feature extraction method employs gray relational grade detection on the frequency bands of EEG and egogram. The gray relational grade is used for extracting the EEG feature. The egogram is extracted for quantifying the subject's personality. The preference patterns generated when the subject is hearing a sound are classified using the nearest neighbor method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data. These results show that the accuracy rate of the preference classification using the proposed method is better than that using the method that does not to consider the subject's personality.

本論文では，音刺激に対する嗜好パターンを脳波分析技術を用いて検出する手法を提案する．脳波分析では，脳波の周波数帯域間の関連性を灰色理論に基づいて分析し，灰色関連度を算出し，脳波の特徴量とみなす．脳波に含まれる個人差を緩和するために，ヒトの性格をエゴグラムを用いて定量化する．脳波の特徴量とエゴグラムを特徴ベクトルとし，嗜好パターンを分類する．実験検証の結果，提案手法の有効性を示すことが出来た．


A Study of Safety Driving Support System focusing on Driver's Head Posture Categorization

In this paper, we analyze drivers head posture during safety verification and propose a method for classifying head posture using two types of unsupervised neural networks: Self-Organizing Maps (SOMs) and fuzzy Adaptive Resonance Theory (ART). The proposed method can generate the optimal number of cluster-generated labels for the target problem. We experimentally assess the effectiveness of the proposed method by adjusting the fuzzy ART network vigilance parameters. In addition, we indicate that drivers head posture during safety verification can be categorized according to their individual properties.

本論文では，ドライバの安全確認行動中の頭部姿勢を分析し，その結果に基づいて2つの教師なしニューラルネットワーク(Self-Organizing Maps(SOMs)とfuzzy Adaptive Resonance Theory(ART))により頭部姿勢をカテゴライズする手法を提案する．提案手法は対象問題に合わせたクラスタ数を決定可能である．実験では，fuzzy ARTのパラメータの変化による頭部姿勢の分類性能を評価した．最終的に，安全確認行動中の頭部姿勢は個人特性に合わせてカテゴライズできる可能性が示唆された．

International Journal of Engineering Research and Technology


A Nonlinear Learning Algorithm for Large Scale Datasets

Nonlinear feature generation techniques are among the most important processing tools in pattern recognition. As linear feature generation techniques, Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA) have been developed and improved in various ways. Simple-FLDA (SFLDA) is an improved version of FLDA and has demonstrated good performance in recognition experiments. In SFLDA, eigenvectors spanning the eigenspace are obtained with simple iterative calculations, unlike the original FLDA which needs to solve eigenvalue problem of a covariance matrix created with all input samples. However, FLDA and SFLDA algorithms are both linear feature extraction methods. Therefore, the steps used for obtaining the eigenspace from an input space might not be suitable with respect to large, complex datasets. For adjustability to such datasets, the kernel trick is applied to many linear feature generation methods, turning them into nonlinear methods. In this paper, we propose Simple Kernel Discriminant Analysis (SKDA) for higher recognition performance by applying a kernel trick to the SFLDA algorithm. Similar to the SFLDA algorithm, SKDA algorithm is composed with simple calculations, but in recognition experiments using the UCI datasets as well as face image dataset; its features equal or surpass those of the SFLDA algorithm.

非線形な特徴生成手法はパターン認識の分野で最も重要なツールの一つである．線形の主成分分析(PCA)とフィッシャー判別分析(FLDA)が様々な方法で改良されており，高性能な特性が評価されている．また，判別分析の近似的手法であるSimple-FLDAも提案されているが，大規模な複雑データに対しては必ずしも妥当な方法ではない．本論文では，Simple-FLDAに対してカーネルトリックを適用して非線形高次元空間に拡張したSimple Kernel Discriminant Analysis (SKDA)を提案する．本手法をUCIデータセットと顔画像データセットに適用し，その非線形での有効性を評価している．

International Journal of Engineering and Innovative Technology


Novel Approximate Statistical Algorithm for Large Complex Datasets

In the field of pattern recognition, principal component analysis (PCA) is one of the most well-known feature extraction methods for reducing the dimensionality of high-dimensional datasets. Simple-PCA (SPCA), which is a faster version of PCA, performs effectively with iterative operated learning. However, SPCA might not be efficient when input data are distributed in a complex manner because it learns without using the class information in the dataset. Thus, SPCA cannot be said to be optimal from the perspective of feature extraction for classification. In this study, we propose a new learning algorithm that uses the class information in the dataset. Eigenvectors spanning the eigenspace of the dataset are produced by calculating the data variations within each class. We present our proposed algorithm and discuss the results of our experiments that used UCI datasets to compare SPCA and our proposed algorithm.

パターン認識の分野では，従来から主成分分析(PCA)が特徴抽出やデータ次元の圧縮に対して多く使われてきた．本論文では，主成分分析(PCA)の近似解法であるSimple-PCAに焦点を当て，その性能を改善する方法を提案している．特に，従来のSimple-PCAに対してクラス情報を付加したアルゴリズムを構成し，学習速度を維持した状態で，優れた特徴抽出機能を実現している．本アルゴリズムの有効性を定量的に評価するために，UCIデータベースを用いて多次元の大規模データに対する特徴抽出性能を評価している．

International Journal of Machine Learning and Computing


Automatic system to remove unpleasant images detected by pupil-size changes.

International Journal of Computer Science Issues


Extraction Method of Brain Regions Using Balloon models for Diagnosis Support of Alzheimer-Type Dementia

Balloon modelsを用いた認知症診断支援のための脳領域抽出法

We intend to construct an image diagnosis support system for Alzheimer-type Dementia (ATD) that extracts temporal lobe regions and an intracranial region as interest regions from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the regions of interest. In this paper, we specifically discuss extraction of regions of interest. The proposed method consists of three steps. First, we emphasis features of an obscure T2-weighted brain image. Second, we set a first contour that approximates a shape of the temporal lobe region to a triangle and apply Balloon models with the added presser force that push the initial contour outside in order to extract a temporal lobe region. Finally, we extract an intracranial region using extracted temporal lobe regions. Our proposed method can extract regions of interest along individual brain features by only a few interactions with three points. We demonstrate the potential of our method using actual diagnosis images. Moreover, we show a possibility to use of atrophy rate at the regions of interest for diagnosis support of ATD.

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


A Basic Study on Selection of Main Image for MR Brain Image Set for Supporting Medical Specialists

専門医の診断支援を目的としたMRI脳画像データセットにおける中心近傍画像選択法に関する基礎的検討

Multiple MR brain images are acquired at once. To begin with, the medical specialists examine a MR brain image, which shows temporal lobes, a frontal lobe, and some important regions for diagnosis, located near the cerebral center. Automatic selection of the main image from MR brain image set is a useful diagnosis imaging support for medical specialists. This paper proposes a method for extracting a ventricle area in order to select a main image. The proposed method works three steps. First, the process using a mode method that extracts sliced areas from original MR brain images. Second, the Otsu's method performs a binary process. Third, a ventricle area is a unique shape and we therefore defined two rectangles: the frontal horn and the posterior horn of a lateral ventricle region. The experimental results revealed that the proposed algorithm was able to accurately extract the ventricle area from the image set.

Journal of the Society od Materials Engineering for Resource of Japan