2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献_第2页

Visualization of brain activation during the performance of attention-demanding tasks 在执行需要注意力的任务时，大脑活动的可视化

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862052

Muthumeenakshi Subramanian, B. Geethanjali, N. Seshadri, V. Bhavana, R. Vijayalakshmi

{"title":"Visualization of brain activation during the performance of attention-demanding tasks","authors":"Muthumeenakshi Subramanian, B. Geethanjali, N. Seshadri, V. Bhavana, R. Vijayalakshmi","doi":"10.1109/ICCI-CC.2016.7862052","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862052","url":null,"abstract":"Attention is the primary cognitive process to induce a response to a stimulus. Maintaining the attentive state continuously for a prolonged period of time is known as sustained attention which is vital for performing any task. The present study aims at evaluating the activation of different brain regions while performing an attention requiring task. A standard attention task called the Visual Continuous Performance test was employed for the study. The analysis is achieved with the help of electroencephalography (EEG) recorded simultaneously during the entire period of execution of task. The task report detailing the errors committed, reaction time is generated automatically and indicates the level of performance. The relative theta and gamma power were significantly higher (p=0.05) during task when compared to that determined during rest, whereas in alpha band the relative power was significantly higher (p=0.05) during rest when compared to task. Event related synchronization (ERS) and Event related Desynchronization (ERD) in relative theta power and relative alpha power respectively was observed particularly in the parietal cognitive processing electrodes (associated with attention). Theta synchronisation and alpha desynchronization is associated with good performance; this was supported by the task performance result which reported a minimum of errors. These event-related changes helped sustain attention and a visualization of the activated brain regions was accomplished for a better depiction of the findings.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127866559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Qualitative analysis of pre-performance routines in throwing using simple brain-wave sensor 用简易脑波传感器对投掷赛前动作进行定性分析

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862033

H. Hiraishi

{"title":"Qualitative analysis of pre-performance routines in throwing using simple brain-wave sensor","authors":"H. Hiraishi","doi":"10.1109/ICCI-CC.2016.7862033","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862033","url":null,"abstract":"This paper describes a qualitative analysis of the concentration level required to throw an object at a specific target, such as the free throw in basketball or darts games, using a simple brain-wave sensor that is a type of electroencephalograph. The qualitative analysis does not focus on quantity, but on qualitative changes, such as increasing, decreasing, or stabilizing. The analysis allows us to clarify the essential features of subjects where standards are individually different, such as brain waves or concentration levels. Therefore, we analyze the differences in concentration levels between experts and novices while throwing. Furthermore, we analyze the influence of concentration levels by pre-performance routines (PPRs), which involve performing certain determined motions before throwing, and are often executed in sports for the purpose of removing stress or raising concentration. The analysis reveals a concentration-stabilizing phenomenon where the concentration level becomes stabilized prior to throwing. We also find that the phenomenon appears more conspicuously in the case of experts and PPRs. This means that a type of PPR exists in the case of experts, and removing stress or raising concentration, both of which are the purpose of PPRs, is similar to stabilizing the concentration gained from brain waves. Therefore, because we can train PPRs by checking the concentration levels, we designed a PPR training tool that uses smart glass, one of the wearable computers.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114382751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying users and activities with cognitive signal processing from a wearable headband 通过可穿戴头带的认知信号处理来识别用户和活动

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862025

Glavin Wiechert, Matt Triff, Zhixing Liu, Zhicheng Yin, Shuai Zhao, Ziyun Zhong, Runxing Zhaou, P. Lingras

引用次数: 13

Simplification and visualization of brain network extracted from fMRI data using CEREBRA 利用CEREBRA对fMRI数据提取的脑网络进行简化和可视化

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862032

Baris Nasir, F. Yarman-Vural

{"title":"Simplification and visualization of brain network extracted from fMRI data using CEREBRA","authors":"Baris Nasir, F. Yarman-Vural","doi":"10.1109/ICCI-CC.2016.7862032","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862032","url":null,"abstract":"In this paper, we introduce graph simplification capabilities of a new tool, CEREBRA, which is used to visualize the 3D network of human brain, extracted from the fMRI data. The nodes of the network are defined as the voxels with the attributes corresponding to the intensity values changing by time and the coordinates in three dimensional Euclidean space. The arc weights are estimated by modeling the relationships among the voxel activation records. We aim to help researchers to reveal the underlying brain state by examining the active regions of the brain and observe the interactions among them. Although the tool provides many features for displaying the fMRI data as a dynamical network, in this study, we have mainly focused on two main features. The first one is the unique graph simplification module that allows users to eliminate redundant edges according to some weighted similarity criterion. The second one is visualizing the output of the external algorithms for voxel selection, clustering or network representation of fMRI data. Thus, users are able to display, analyze and further process the output of their own algorithms.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123594149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a cognitive vehicle system for simulation of driving behavior 模拟驾驶行为的认知车辆系统的开发

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862065

M. T. Chan, Christine W. Chan, Craig M. Gelowitz

引用次数: 0

Communication channel analysis and simulation of medical implanted electronic devices based on the volume conduction 基于体积传导的医用植入电子器件通信信道分析与仿真

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862072

Lixiao Feng, Jun Peng, Guorong Chen, Chengyuan Chen, Dedong Tang

引用次数: 0

MetaThink: A MOF-based metacognitive modeling tool MetaThink:一个基于mof的元认知建模工具

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862059

M. Pineres, D. A. Diaz, D. Josyula, B. JovaniA.Jiménez

引用次数: 0

A Sparse Temporal Mesh Model for brain decoding 脑解码的稀疏时间网格模型

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862035

Arman Afrasiyabi, Itir Önal, F. Yarman-Vural

{"title":"A Sparse Temporal Mesh Model for brain decoding","authors":"Arman Afrasiyabi, Itir Önal, F. Yarman-Vural","doi":"10.1109/ICCI-CC.2016.7862035","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862035","url":null,"abstract":"One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the voxel selection methods with the mesh learning method. We, first, select the “most discriminative” voxels using the state-of-the-art feature selection methods, namely, Recursive Feature Elimination (RFE), one way Analysis of Variance (ANOVA) and Mutual Information (MI). After we select the most informative voxels, we form a star mesh around each selected voxel with their functional neighbors. Then, we estimate the mesh arc weights, which represent the relationship among the voxels within a neighborhood. We further prune the estimated arc weights using ANOVA to get rid of redundant relationships among the voxels. By doing so, we obtain a sparse representation of information in the brain to discriminate cognitive states. Finally, we train k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers by the feature vectors of sparse mesh arc weights. We test STMM architecture on a visual object recognition experiment. Our results show that forming meshes around the selected voxels leads to a substantial increase in the classification accuracy, compared to forming meshes around all the voxels in the brain. Furthermore, pruning the mesh arc weights by ANOVA solves the dimensionality curse problem and leads to a slight increase in the classification performance. We also discover that, the resulting network of sparse temporal meshes are quite similar in all three voxel selection methods, namely, RFE, ANOVA or MI.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126502078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Design and implementation of user-oriented video streaming service based on machine learning 基于机器学习的面向用户的视频流服务的设计与实现

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862023

Makoto Oide, Akiko Takahashi, Toru Abe, T. Suganuma

引用次数: 1

Deep reasoning and thinking beyond deep learning by cognitive robots and brain-inspired systems 认知机器人和大脑启发系统超越深度学习的深度推理和思考

2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2016-08-01 DOI: 10.1109/ICCI-CC.2016.7862095

Yingxu Wang

{"title":"Deep reasoning and thinking beyond deep learning by cognitive robots and brain-inspired systems","authors":"Yingxu Wang","doi":"10.1109/ICCI-CC.2016.7862095","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862095","url":null,"abstract":"Recent basic studies reveal that AI problems are deeply rooted in both the understanding of the natural intelligence and the adoption of suitable mathematical means for rigorously modeling the brain in machine understandable forms. Learning is a cognitive process of knowledge and behavior acquisition. Learning can be classified into five categories known as object identification, cluster classification, functional regression, behavior generation, and knowledge acquisition. A fundamental challenge to knowledge learning different from the deep and recurring neural network technologies has led to the emergence of the field of cognitive machine learning on the basis of recent breakthroughs in denotational mathematics and mathematical engineering. This keynote lecture presents latest advances in formal brain studies and cognitive systems for deep reasoning and deep learning. It is recognized that key technologies enabling cognitive robots mimicking the brain rely not only on deep learning, but also on deep reasoning and thinking towards machinable thoughts and cognitive knowledge bases built by a cognitive systems. A fundamental theory and novel technology for implementing deep thinking robots are demonstrated based on concept algebra, semantics algebra, and inference algebra.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134441742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21