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.


Semi-Supervised Learning for Auditory Event-Related Potential-Based Brain-Computer Interface

A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets - AMUSE and PASS2Dusing the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.


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．


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


Evaluation of Orientation Performance of Attention Patterns for Blind Person

Tactile walking surface indicators (TWSIs) are installed on footpath to support independent travel for the blind. There are two types of TWSIs, attention patterns and guiding patterns. The attention pattern is usually installed at the crosswalk entrances. The direction of the crossing can be acquired by the row of the projection of the attention pattern through the soles of the shoes. In addition, truncated domes or cones of the attention pattern were arranged in a square grid, parallel or diagonal at 45 degrees to the principal direction of travel. However, the international standard organization (ISO) allows a wide-ranging size. In this research, the direction indicating performance was compared at the same intervals for the five diameters specified by the international standard. As a result of the experiment, the diagonal array does not indicate the direction of travel, but the projection row does indicate the direction of travel in the parallel array. When the attention pattern is installed at a crosswalk entrance, a parallel array should be installed in the direction of the crossing.

Studies in Health Technology and Informatics


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)


Emitting LED block at crosswalk entrance for visually impaired persons

Visually impaired persons require tactile walking surface indicators (TWSIs) for independent walking. However, if these blocks are installed following guidelines for constructing TWSIs, it's unkind to users. Therefore, we propose LED blocks installed at crosswalk entrances for blind and visually impaired persons. These blocks can be detected through the soles of shoes and through use of residual vision. This study conducted an experiment with amblyopic examinees in which we evaluate the visibility of the LED blocks.


Development of Augmentative and Alternative Communication Assessment Tools for Patients with ALS

Amyotrophic lateral sclerosis (ALS) is a progressive, intractable disease that causes the body to gradually become weaker. A communication device for the handicapped with a high degree of disability (see Fig. 1) is often used for a seriously ill ALS patient. However, the degree of the symptoms differs in each patient, so it is necessary to select a suitable input switch. Moreover, the selection of the switch is greatly influenced by differences in knowledge and experience, so support is often advanced preceded by a subjective evaluation [1]-[6]. This research developed an evaluation apparatus capable of measuring the movement of the finger from the displacement of the lever using a sensor built into the input switch. As a result, appropriate support is expected to evaluate patient's remaining function based on objective data when the input switch is selected.

筋萎縮性側索硬化症(ALS)は，身体機能が徐々に衰えていく進行性の難病である．重度のALS患者には，重度障害者用意思伝達装置が用いられることが多い．しかし，症状の度合いが患者ごとに異なるため，適した入力スイッチを選定する必要がある．また，スイッチの選定は知識・経験の差に大きく影響されるため，主観的な評価で支援が進められることが多い．本研究では，入力スイッチにセンサを組み込むことでレバーの変位から指の動きを計測できる評価器具の開発を行った．これにより，入力スイッチの選定を行う際に，患者の残存機能を客観的なデータ根拠に基づいて評価し，適切な適合支援が可能になることが期待される．


Walking Characteristics of Persons with Visually Impairment Crossing Intersections with Audible Pedestrian Signals

The authors have been continuously researching tactile walking surface indicators and audible pedestrian signals used for crossing intersections. A pedestrian walks to the opposite side of the crosswalk using audible pedestrian signals. The position where the audible pedestrian signal is set up clearly influences the pedestrian's path. We performed an intersection crossing experiment with road noise for different positions where the audible pedestrian signal was set up. Vehicle road noise is thought to influence walking pedestrians. However, we could not clearly separate the difference of where the audible pedestrian signal was set up from the influence of the road noise. We therefore conducted the experiment with no road noise in the intersection to determine the difference of the position where the audible pedestrian signal was set up. The research results were compared with and without road noise to specify the road noise influence.


Visibility of LED Blocks Mounted on Crosswalk Boundaries for low Visual Capacity

According to the WHO, about 80% of visually impaired persons in the world are with low visual capacity. It is considered that LVs' safe independent mobility will be improved as employing Tactile Walking Surface Indicators (TWSIs). In the night, guidelines for constructing warning blocks, a kind of tactile surface indicator, have not been well established. Therefore, crossing crosswalks is one of the most dangerous situations for visually impaired persons. We thus developed novel LED blocks that indicate crosswalk boundaries between sidewalks and roadways. This study addresses the LED blocks under various environmental illuminations and illumination from the opposite side of the crossing.


Strain magnitude dependent intracellular calcium signaling response to uniaxial stretch in osteoblastic cells

In osteoblast cells, cells change their intracellular Ca2+ concentration ([Ca2+]i) as the response to mechanical stimuli. Although it has been reported that osteoblast cells responded to many kinds of mechanical stimuli including stretch of substrate, shear stress in fluid flow, direct indentation of glass microneedle and hydrostatic pressure etc., the detail of the characteristics of intracellular calcium signaling response to substrate stretch still remains unclear because motion artifact during stretch application causes out of focus plane and observation area, and complicates the in situ time lapse observation of change in [Ca2+]i in a single cell level. In this study, we combined our originally developed cell stretching MEMS device with the ratiometric microscopy method with two kinds of visible wavelength calcium indicator dyes. The cell stretching micro device and the ratiometric method reduce the influence of motion artifact during stretch application, and enable us to quantitatively evaluate the characteristics of cellular calcium signaling response to stretch. MC3T3-E1 osteoblastic cells were plated onto the cell stretching micro device and fluorescently labeled by Ca2+ indicator Fluo 3 and Fura Red. A uniaxial stretch with three magnitudes of strain 5%, 10% and 15% with constant strain rate were applied to the cells, and in situ time lapse observation of cellular calcium signaling response to stretch was conducted with high temporal and spatial resolution. We succeeded in obtaining time lapse fluorescent image sequences during stretch application without excessive out of focus and blank time. The results revealed that MC3T3-E1 cells change the intensity of calcium signaling response to stretch according to the stretch strain magnitude. As stretch strain magnitude was increased, the amount of change in fluorescent ratio value of Ca2+ indicators in stretched cells also increased. This result suggests the possibility that osteoblastic cells can sense the magnitude of mechanical stimuli at upstream of mechanotransduction pathway such as influx of extracellular Ca2+.

Journal of Biomechanical Science and Engineering


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．


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．


Electroencephalogram Analysis of Mechanisms Underlying Brain Activity during Voluntary Movement

This paper proposes an electroencephalogram (EEG)-based method for identifying the mechanism underlying brain activity during a voluntary movement. Our final goal is to develop a rehabilitation assistance system that can be used on a daily basis by observing a patients degree of functional recovery. In order to assist in ambulation rehabilitation, the mechanism related to the voluntary movement need to be identified. As a first step, we elucidated the mechanism underlying brain activity. The proposed method involves EEG recording, blind source separation, noise reduction, and feature enhancement. The EEG device has dry-type sensors. Independent component analysis is used to delink the signal. Median morphological filters are used to reduce EEG noise and emphasize the feature signal. To demonstrate the effectiveness of the proposed method, we conducted experiments using real EEG data.

ブレイン・コンピュータ・インタフェースによる歩行アシストシステム構築のために，随意運動時における大脳皮質の活動メカニズムを分析する手法を提案する．提案手法は，独立成分分析とメディア・モルフォロジーフィルタを用いてノイズ除去および特徴を強調する．実験検証の結果，提案手法の有効性が示され，かつ，随意運動時の大脳皮質活動にも個人差が含まれることが確認できた．

International Journal of Bioscience, Biochemistry, Bioinformatics (IJBBB)


Difference in Road Surface Recognition by Disease and Grades of The Low Vision

In our previous studies, Lateral Bands that were placed in both side of Tactile Walking Surface Indicators, and the result of clinical testing of Lareral Bands indicated the effectiveness for the Low Vison to show walking way. In this paper, relation between visibility and miss-recognition was studied from the different point of view in date of the previous study. Influence both by the difference in visual impairment and the difference in grades of disability was also studied. As the results, it was suggested that 30% of luminance contrast was the optimum value for both keeping visibility of Tactile Walking Surface Indicators with Lateral Bands and producing no miss-recognition.

我々の以前の研究で，視覚障害者誘導用ブロックの両側に置かれた側帯と側帯の医学的な実験結果は視覚障害者が道を歩いていて見つけるために有効であることが示された．本論文では，視認性と誤識別の関係を前の研究のデータを異なった観点から検証した．視覚障害者の症状の違いと等級の違いによる影響も検証した．結果として，30%の輝度コントラストが側帯と製造された点字ブロックの視認に誤識別しないことを担保するための最適値であることが示された．


Verification of LED Blocks used at Crosswalk Entrances for Persons with Visually Impairment

The eyesight of a person with low visual capacity (LV) might decrease remarkably under low-light conditions such as the morning, evening, and nighttime, decreasing their opportunities to go out. Research and development underway to ensure safe walking for LVs examined a walking guidance system in which LED blocks are embedded into the road at edges of warning blocks installed at crosswalk entrances. The LED block can be detected with the LVs residual vision. The detectability of this LED block was evaluated by subjective experiments performed by visually impaired and blind persons.

ロービジョン者の中には，朝夕や夜間などの低照度環境下において視力による視認性が著しく低下する場合がある．このことが，ロービジョン者の外出の機会の減少させる一因となっている．視覚障害者の安全な歩行を確保するため，視覚障害者誘導支援システムに関する様々な研究・開発が行われている．本研究では，LED発光ブロックを路面に埋め込む歩行誘導システムについて検討した．横断歩道口用発光ブロックを横断歩道口の注意喚起ブロックの端に設置した．この発光ブロックはロービジョン者にとっては，残存視力で知覚することができる．この発光ブロックの知覚に関する性能特性を被験者実験によって評価した．被験者実験はロービジョン者と盲人によって実施した．


Research on the Occupational Ability Evaluation Technique in the Group Work of Intellectually/Mentally Handicapped Persons by MODAPTS Method

The first objective is to analyze the characteristics of the intellectually/mentally disabled more deeply in order to help the government and companies to understand them and the employment rate to increase. The second objective is to review the work evaluation method of the intellually/mentally disabled anew through this evaluation in order to trigger the research on the intellectually/mentally disabled to be activated. The result of the verification performed by this research has showed that the success or failure in a continuous company employment is more significantly related to the factors of ``surrounding healthy persons'' which are external factors than the factors arising from the disability of a disabled person.

本研究の第一の目的は，政府と会社が彼らを理解し就職率が増加することより深く支援するために，知的/精神障害者の特性を分析することである．第二の目的は，知的/精神障害者が活動的になることを研究が引き金になってこの評価を新たに通して，知的/精神障害者の作業評価方法を見直すことである．本研究の検証の結果，会社の雇用の阻害要因が障害者の障害から起こる要素より外部の要素である「強周囲の健康な人」の要素にかなり関連するのを示した．


Continuity of Tactile Walking Surface Indicators and Audible Pedestrian Signals at Crosswalks

Crossing an intersection is one of the most risky actions for a visually impaired person. A visually impaired pedestrian is guided to the crosswalk entrance by the tactile walking surface indicators "TWSIs". It is then important that he/she safely and smoothly cross the intersection using the audible pedestrian signal. However, neither connectivity nor the continuity between the TWSIs and the audible pedestrian signal travel support systems has been verified. The purpose of this research is to verify this connectivity and continuity. It should be possible to acquire basic scientific data for developing guidelines from the findings of this research.

視覚障害者が交差点を横断するのは，最も危険な行動の1つである．視覚障害を持つ歩行者は視覚障害者誘導用ブロック「TWSIs」によって横断歩道入り口に誘導される．視覚障害者が歩行者用音響信号を利用して安全にかつ円滑に交差点を横断することが重要です．しかしながら，TWSIsと音響式歩行者信号との歩行支援システム間の接続性や連続性が確保させていない．本研究の目的は，この接続性と連続性について確かめることある．本研究の知見から指針設計のための基本的なの科学的データを取得することが可能となる．


Visualization Method to Confirm Relationships among Optimal Solutions

This paper proposes method to visualize the relationship between the gene and objective values of all individuals in a Pareto solution. The gene and objective values are expressed as color and gray scales, respectively. The rules of visualization apply three normalization techniques for coloring the gene values and the objective function values, respectively. The gene values normalize using maximum and minimum values over all gene of all suboptimal solutions, in each gene and in each individual, respectively. The objective values normalize using maximum and minimum values in each objective function. We define the visualization space based on the visualization rules. The information visualization technique in this paper has the advantage of indicating the Pareto solution and its details in one frame. To show the effectiveness of the proposed method, we apply the proposed method to benchmark problems. We found that it is possible to confirm the Pareto solution, its details and relationships among those easily in one frame.

パレート最適解間の関連性を可視化する手法を提案する．提案手法では，個体の適応度と個体の遺伝子の値を見える化することで，最適解間の関連性を一目で把握可能になる．見える化技術には疑似カラー法を適用している．提案手法の有効性検証するために，多目的ナップサック問題を適用した結果，提案手法の有用性が確認できた．


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


Analysis of Individual Difference in EEG using Self-organizing Map

自己組織化マップを用いた聴覚刺激時における脳波に含まれる個人差に関する一考察

This paper introduces a method to analyze an individual difference in an electroencephalogram (EEG) using a self-organizing map (SOM). The EEG recording position is a left prefrontal pole (Fp1) in the international 10-20 system. The device for recording the EEG uses dry-type sensor and a small number of electrodes. The EEG data is analyzed through the FFT and EEG features are extracted by calculating the time average of the power spectrum of five frequency bands which are theta, low-alpha, high-alpha, low-beta and high-beta, respectively. In order to confirm the individual difference in the EEG, the EEG patterns are classified using the SOM. The EEG patterns are based on human responses on sounds listened to. Finally, we conduct experiments using real EEG data. The experimental results suggest that it is possible to express the individual differences in the EEG using SOM.

本論文では，自己組織化マップ(self-organizing map: SOM)を用いて脳波の個人差を見える化し，聴覚刺激時における脳波の個人差に関して考察する手法を紹介する．脳波の計測箇所は左前頭極付近とし，周波数分析の結果を脳波の特徴量とする．紹介する手法の有効性を検証するために，聴取音に対するヒトの嗜好を分類する問題に適用する．実験的検証の結果，紹介手法の有用性が確認できた．


Interindividual Difference Analysis in Prefrontal Cortex EEGs Based on the Relationship with Personality

In this study, we propose a method to analyse interindividual differences in electroencephalograms (EEGs) taken while a subject listened to music, which is based on the relationship of these EEGs to human personality. The frequencies of the EEGs that are analysed have components that contain both significant and immaterial information as well as different levels of importance. We express the different levels of importance through the weight values of frequencies using a real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are detected using the $k$-nearest neighbour method. We assume that differences between detection results for EEG patterns with the highest and lowest recognition accuracies show interindividual differences. Moreover, ego analysis based on psychological testing is used to analyse personality, and the ego score is determined using a questionnaire. Finally, we discuss the relationship between personality and interindividual differences observed in experimental EEGs. An interesting tendency of a person with a combined ego type is that he or she has a unique response to negative stimuli compared with that of positive stimuli.

聴取音楽に対する感受性の違いを分析する方法を提案する．提案手法は脳波分析部，パーソナリティ分析部，および聴取音楽の印象分類ぶで構成される．脳波分析部では，遺伝的アルゴリズムを用いて脳聴取音楽の印象分類に有用な周波数帯域を選定する．パーソナリティ分析部では，心理テストを用いてヒトの性格を定量化する．印象分類部では，k近傍法を用いて聴取音楽に対する印象を分類する．個体間差を分析するために，脳波による聴取音楽の印象分類結果と定量化された性格との関連性を分析した．その結果，ネガティブな印象の検出が容易な被験者は，内向的な性格である可能性が示唆された．


Lower back pain survery during care work and Bed care load assessment --- 富山県腰痛予防対策推進研修会腰痛アンケート結果から考えられるベッド介助作業負担の評価

介助作業中の腰痛調査とベッド介助負担評価 --- 富山県腰痛予防対策推進研修会腰痛アンケート結果から考えられるベッド介助作業負担の評価

We conducted a questionnaire about lower-back-pain to the participants at the Toyama lower-back-pain measure promotion study session (1925 persons). The question asked the presence of lower back pain and investigated what kind of lower back pain they had in respect of a care work place. Furthermore, the IEMG and the breathing gas analyzing apparatus analyzed the weight load (10kg) in the position which changed the height in the bed depended for lifting. About 60 percent of caregivers had a lower-back-pain person by a caregiver. When the persons who had lower back pain in the past were included, about 80 percent of them had lower back pain. Caregivers often felt lower back pain when engaging in care work at the bedside. The causation of the onset of chronic lower back pain was "the work which kept their body with half-sitting posture for a long time." In care work with a bed, work in the height of the waist reduced the load for a line active mass and breathing gas volume. The present care work had the high risk of causing lower back pain. To decrease the risk, the care worker should do bed care work at the height of the waist. This result should be adopted as a job standard.

本調査研究では，腰痛アンケート調査(1,925名)を実施し，腰痛の有無と介助作業場面でどのような腰痛が発生するかについて介助動作場面の問題点を調査した上で，その結果からベッド介助作業での高さを変えた位置での持ち上げによる重量負荷(10kg)を筋電計と呼気ガス分析装置を用いて分析した．結果介護者の腰痛発症者が6割近くあり，既往者を含めると8割近くが腰痛を訴えていた．腰痛が発生しやすい介助動作は，ベッドでの介助場面であった．腰痛発生の原因は，中腰で長く続く作業であり，ベッドでの介助作業は腰の高さでの作業が筋活動量・呼気ガス量ともに負担を減らすことになった．ベッド介助作業は腰痛へのリスクが高く，リスクを減少させるには，腰の高さのベッド介助作業を作業標準とすべきであることがわかった．


Mis-recognition of Road Surface by The Low Vision

The low vision sometimes miss-recognize road surface as holes or ditches. The purpose of our research is to find parameters of road surface that causes this kind of miss-recognition. As the preliminary result of our research, this paper describes how the low vision recognizes road surface incorrectly. Fifty-nine low vision persons were participated as subjects in these experiments. Visibility of road surface's line that have different parameters in width, luminance contrast, and illumination was experimentally studied. One of the three experiment, eight lines that have different parameters respectively were showed to a subject at a time. The lines were arranged in ascending order. At the end of each experiment, comments from a subject were obtained.

弱視者は時々路面を穴や溝として誤識別する．この場合の誤識別に起因する路面の要因を見つけることが本研究の目的である．本研究の予備結果として，本論文は弱視者がどのように路面を誤認識するかを説明する．弱視者59名を被験者として実験に参加した．幅，輝度比，および照明の異なったパラメータの路面の線の視認性を実験で検証を行った．三つの内の一つの実験では，被験者にそれぞれ異なったパラメータを持った8本の線を一度に見てもらう．線は昇降順に配置されている．それぞれの実験の終わりに，被験者からの回答を得た．


An evaluation of tactile walking surface indicators for the visually impaired

Tactile Walking Surface Indicators (TWSIs) are a vital walking aid for the visually impaired, especially those with complete blind person, but the blocks are still not available on all walkways. This study was performed with visually inpaired person to determine to what degree TWSI (in particular, blocks with raised parts to indicate caution) contribute to direction determination, with the goal of improving the utility of TWSIs. By comparing drift after 5m of straight line travel, significant differences were seen between the existence and nonexistence of border elevation for both square and zigzag patterns, confirming that determining direction when using square patterns over zigzag patterns.

視覚障害者誘導用ブロック(TWSIs)は視覚障害者，特に全盲者のための重要な歩行支援用品である．しかし，点字ブロックがすべての歩道で有効ではない．本研究はTWSI(特に，注意喚起ブロック)が方向の決定に貢献する割合を決定するため視覚障害者で検証を行った．TWSIsの形状を改善することが目的である．直線を5m歩行した後，その偏軌を比較することによって，正方配列と千鳥配列の方向提示性能の違いがあるかの検証を行う．方向提示性能は千鳥配列より正方配列が有意であることが確認できた．


Study on Model for Predicting the Intra-Individual Difference in Left Prefrontal Pole Electroencephalogram Variability and Its Evaluation

This paper introduces a novel statistical model to estimate an intra-individual difference in left prefrontal cortex electroencephalogram (EEG) activities, and a method for evaluating the proposed model. It is known that an EEG contains the individual characteristics. However, extraction of these individual characteristics has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the first factor loading that is calculated by eigenvalue decomposition as the EEG feature. Furthermore, we use k-nearest neighbor (kNN) algorithm for evaluating the proposed model and the extracted EEG features. In general, the distance metric used is Euclidean distance. It is possible that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform an experiment using real EEG data.

脳波の特徴抽出精度向上ための手法と抽出された脳波の特徴データを客観的に評価する方法を提案した．特徴抽出は，一般的な潜在構造モデルのモデル誤差を個人誤差と定義したモデルにより行なう．その際に，未知のパラメータである個人誤差をRGAにより推定している．客観的評価は，目視できる3次元空間へ射影する方法を提案している．そして音楽聴取時の脳波データを用いて，提案手法の有効性を検証した．


Aligning Real Space with the 2D Model on See-through HMD Using GA with Interactive Handling

In this paper, we propose an approach for the aligning the real space with the 2D model. In our approach, we use a monocular see-through head mount display (HMD) and a virtual reality (VR) sensor. This sensor can measure three degree of freedom (3DOF) angle of head directions. However, this sensor has some errors, which are divided into three types of errors. The first one is the drift errors of the sensor. Second one is the false of the sensor detections. These errors are caused by the sensor itself. The last one is caused by personal visual features. Therefore, we solve these problems with the genetic algorithm (GA) and threshold processing. In this system, we incorporate personal subjective evaluation into the optimization system to cut down processing speed and get close to the answer. Users handle the model interactively to align with real space using air mouse.

本論文では，実空間を2次元モデルに合わせる手法を提案する．提案手法では，単眼シースルーヘッドマウントディスプレイ(HMD)とバーチャルリアリティ(VR)センサを使用する．このセンサは，頭部方向の3自由度(3DOF)角度を測定可能である．しかしながら，このセンサには3種類の誤差に分けている．誤差は，センサーのドリフト，センサの誤検出，個人の視覚的特徴の誤差である．提案手法では，遺伝的アルゴリズム(GA)と閾値処理を用いてこれらの問題を解決する．このシステムでは，個人の主観評価を最適化システムに組み込み，処理速度を落として答えに近づける．ユーザーはエアーマウスを使用してモデルをインタラクティブに操作して実空間に合わせる．実験的検証の結果，良好な結果を得るに至った．


A model for extracting personal features of an electroencephalogram and its evaluation method

脳波の個人特性推定モデルとその評価方法に関する一考察

This paper introduces a model for extracting features of an electroencephalogram (EEG) and a method for evaluating the model. In general, it is known that an EEG contains personal features. However, extraction of these personal features has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the EEG feature as a first factor loading, which is calculated by eigenvalue decomposition. Furthermore, we use a k-nearest neighbor (kNN) algorithm for evaluating the proposed model and extracted EEG features. In general, the distance metric used is Euclidean distance. We believe that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance, and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform a computer simulation using real EEG data.

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)


Extraction of EEG Characteristics While Listening to Music and Its Evaluation Based on a Latency Structure Model with Individual Characteristics

EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method.

本論文では，以下の3点に着目し，脳波の特徴精度向上のための手法を提案した．1点目は，脳波インタフェース応用を考慮し，一つのセンサから得られる情報のみを使用する．2点目は，脳波の問題点の一つである個人差による特徴抽出精度の低下軽減のため，一般的な統計学モデルの一つである潜在構造モデルのモデル誤差を，個人誤差と定義したモデルの考案である．なお，個人誤差は未知のパラメータであるため，実数値GAを用いて最適化している．3点目は，特徴抽出精度を客観的に評価するため，可視化空間へ写像することである．さらに，音楽聴取時の脳波データを用いて，聴取曲を分類する問題に適用し，有効性を検証した．


Objectively Psychological Evaluation Using the EEG

The animal assisted therapy (AAT) has been known to have psychological and social effects on human being. However, scientific research is insufficient yet. Because it is difficult to have done in the medical clinic. Furthermore, the government cannot allow as a medical care because of nothing of clear proof. Therefore, in order to show a scientific basis of the AAT effects, we analyze the electroencephalogram (EEG) in the case of using the AAT. The EEG is generated by the brain activity, so that there is the correlation between the EEG and mentality. First of all, we define the healing by questionnaires. Secondly, we extract the correlation between the EEG features and mentality. Finally, we investigate the correlation between EEG features and the healing.

アニマルセラピーは人間に対する心理的および社会的影響を有することが知られている． しかし，科学的研究はまだ不十分である． それは内科診療所内での実施が難しいからです． さらに政府は明確な証拠がないため，医療として許可することはできない． したがって，AAT効果の科学的根拠を示すために，AATを脳波(EEG)を分析することで評価する． EEGは脳活動を頭皮上から観測した電気信号であるため，EEGと精神性との間に相関があると考えられる．実験的検証では，まず，アンケートによる治癒を定義します． 第2に，脳波の特徴と精神性との相関を抽出する． 最後に，EEGの特徴と癒しの相関を調べる．


Detection Method of Music to Match Users Mood in Prefrontal Cortex EEG Activity

前頭前野の脳波を用いた気分一致曲の検出法

In this article, we propose a method for detecting music to match a users mood in prefrontal cortex electroencephalogram (EEG) activity. The frequencies of the EEG analyzed are the components that contain significant and immaterial information. We focused on the significant frequency combinations. These frequency combinations are thought to express personal features of EEG activity. The proposed method calculates the percentage of the spectrum of these frequency combinations and evaluates whether the music matches the users mood through a simple threshold processing. A genetic algorithm (GA) is used to specify the frequency of personal features on the EEG.

ユーザの気分と一致する曲を脳波を用いて自動で検出する手法を提案する．脳波は複数の要因が複雑に絡み合う個人差の著しい時系列データである．脳波の周波数成分には重要な情報を多く含む成分とそうでない成分とが共存している．本論文では脳波の個人差を周波数成分の組み合わせにより表現することで，個人差による脳波ばらつきの抑制および気分一致曲の検出精度向上を試みる．また，気分一致曲は，遺伝的アルゴリズムによって選定された個人特性を表わす周波数成分群を用いて，パワースペクトル対比を演算し，単純な閾値処理により検出される．提案手法の有効性を検証した結果，気分不一致曲の正答率が70%∼90%以上と比較的良好な結果を得ることができた．


EEG Characteristic Extraction Method of Listening Music and Objective Estimation Method Based on Latency Structure Model in Individual Characteristics

個人特性を考慮した潜在構造モデルによる音楽聴取時の脳波特徴抽出法と客観的評価方法

電気学会論文誌C (電子，情報，システム部門誌)


Proposal of the EEG Analysis Method Using the Individual Characteristic of the EEG

脳波の個人特性を考慮した脳波分析法の提案

本論文は，音楽を聴いている場合の脳波を解析することにより，脳の状態を推定する手法を提案している．特に，遺伝的アルゴリズムによる特徴選択と因子分析による特徴解析により脳波分類に適した個人特徴を抽出し，ニューラルネットワークにより学習識別する手法を提案している．音楽鑑賞時における実際の脳波信号に対する計算機シミュレーションにより，本手法の有効性を定量的に評価している．

電気学会論文誌C (電子，情報，システム部門誌)


Research on continuity among assistive devices for personal mobility of visually impaired persons around crossings

Tactile walking-surface indicators (TWSIs) are necessary for visually impaired persons to walk independently. Audible pedestrian signals are also necessary for them to cross intersections. TWSIs support approaching the intersection, and audible pedestrian signals support crossing the intersection. However, the support of audible pedestrian signals may not necessarily be related to the support of the TWSIs. This research sought to verify how visually impaired persons are guided by different positions where the audible pedestrian signals are set up.

視覚障害者誘導用ブロックは視覚障害者の単独歩行には重要である．音響式方向者信号は同様に交差点の横断するために重要である．点字ブロックは交差点までを誘導し，音響式方向者信号機は交差点を横断することを支援する．しかしながら，音響式歩行者信号機の支援は点字ブロックの支援と連携をとっていないことがある．本研究では，音響式歩行者信号機を設置される場所の異なる位置で視覚障害者が如何に誘導されるかを検証することである．