发布求助
文献互助 智能选刊 最新文献

2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)最新文献

筛选
英文 中文
Fixed-point CCA algorithm applied to SSVEP based BCI system 基于SSVEP的BCI系统中的定点CCA算法
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609173
Pujie Zheng, Xiaorong Gao
{"title":"Fixed-point CCA algorithm applied to SSVEP based BCI system","authors":"Pujie Zheng, Xiaorong Gao","doi":"10.1109/CCMB.2013.6609173","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609173","url":null,"abstract":"Canonical correlation analysis (CCA) has already been used to develop an on-line Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) system with high performance and stability. In this study, we proposed a fixed-point CCA algorithm which can be implemented in the embedded processors. It allows the implementation of low power-consumption portable BCI systems without PCs. It was mathematically proved that no overflow problem would occur during the entire process of this fixed-point algorithm. It was also shown that this algorithm could achieve a high calculation precision through the off-line SSVEP dataset. Finally, a number of on-line SSVEP based BCI experiments were conducted to demonstrate the speed of this algorithm. With a 240 MIPS processor, it merely cost 89ms for our algorithm to discriminate between 6 frequencies. The speed was fully compatible for the application of on-line SSVEP based BCI systems.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114442906","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
Discovering the learned rules of dress collocation inside neural network mechanism 发现神经网络机制中习得的服装搭配规律
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609159
Yi-Chun Lin, Chao-I Tuan, C. Liou
{"title":"Discovering the learned rules of dress collocation inside neural network mechanism","authors":"Yi-Chun Lin, Chao-I Tuan, C. Liou","doi":"10.1109/CCMB.2013.6609159","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609159","url":null,"abstract":"This study is to capture the implicit rules of dress collocation by means of neural network modelling and analyses of the trained hidden structure. First, a multi-layer network model is adapted for training, where the input data are features designed by experiments to represent the various dressing styles of our selected nine fashion brands. Then we introduce a technique to display the inner categorization of the trained network model by a tree structure. From this, we discover the hidden rules of neural network models, and reveal the potential of local modification and correction without re-training the whole model.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123313818","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
Common spatial pattern and linear discriminant analysis for motor imagery classification 运动意象分类的常见空间模式与线性判别分析
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609178
Shang-Lin Wu, Chun-Wei Wu, N. Pal, Chih-Yu Chen, Shi-An Chen
{"title":"Common spatial pattern and linear discriminant analysis for motor imagery classification","authors":"Shang-Lin Wu, Chun-Wei Wu, N. Pal, Chih-Yu Chen, Shi-An Chen","doi":"10.1109/CCMB.2013.6609178","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609178","url":null,"abstract":"A Brain-Computer Interface (BCI) system provides a convenient way of communication for healthy subjects and subjects who suffer from severe diseases such as amyotrophic lateral sclerosis (ALS). Motor imagery (MI) is one of the popular ways of designing BCI systems. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. We have also experimented with MI data generated in our lab. The proposed system is found to produce good results. In particular, using our EEG data for MI movements, we have obtained an average classification accuracy of 80% for two subjects using only 9 channels, without any feature selection. This proposed MI-based BCI system may be used in real life applications.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382343","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: 54
Spatial filter design based on re-estimated projection matrices 基于重估计投影矩阵的空间滤波器设计
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609174
Xinyang Li, S. Ong, Yaozhang Pan, K. Ang
{"title":"Spatial filter design based on re-estimated projection matrices","authors":"Xinyang Li, S. Ong, Yaozhang Pan, K. Ang","doi":"10.1109/CCMB.2013.6609174","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609174","url":null,"abstract":"In this paper, motor imagery electroencephalograph classification problem is investigated and a method which modifies the projection matrix is proposed based on common spatial pattern analysis. Exceptional samples are detected through examining the features generated by the projection matrix in the first place, which are special in terms that the projection matrix in common spatial pattern analysis fails to extract discriminant features from them. Projection matrices for exceptional trials are re-estimated and integrated together to form the final projection model. Based on this integrated model, feature extraction is carried out and classification follows by employing support vector machine. The validity of the proposed method is verified through experiment studies. Two data sets that consist of two classes are used, and results show that the proposed method generates more discriminant features.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128210511","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
Unsupervised short-term covariate shift minimization for self-paced BCI 自定步脑机接口的无监督短期协变量移位最小化
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609172
Raheleh Mohammadi, A. M. Far, D. Coyle
{"title":"Unsupervised short-term covariate shift minimization for self-paced BCI","authors":"Raheleh Mohammadi, A. M. Far, D. Coyle","doi":"10.1109/CCMB.2013.6609172","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609172","url":null,"abstract":"A major challenge for Brain Computer Interface systems (BCIs) is dealing with non-stationarity in the EEG signal. There are two types of EEG non-stationarities 1) long-term changes related to fatigue, changes in recording conditions or effects of feedback training which is addressed in classification step and 2) short-term changes related to different mental activities and drifts in slow cortical potentials which can be addressed in the feature extraction step. In this paper we use a covariate shift minimization (CSM) method to alleviate the short-term (single trial) effects of EEG non-stationarity to improve the performance of self-paced BCIs in detecting foot movement from the continuous EEG signal. The results of applying this unsupervised covariate shift minimization with two different classifiers, linear discriminant analysis (LDA) and probabilistic classification vector machines (PCVMs) along with two different filtering methods (constant bandwidth and constant-Q filters) show the considerable improvement in system performance.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994445","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: 8
Neural representation and identification of reaching targets by spike trains in motor cortex 运动皮层中尖峰序列到达目标的神经表征和识别
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609176
Zhiming Xu, K. Ang, Cuntai Guan, Huynh Thai Hoa
{"title":"Neural representation and identification of reaching targets by spike trains in motor cortex","authors":"Zhiming Xu, K. Ang, Cuntai Guan, Huynh Thai Hoa","doi":"10.1109/CCMB.2013.6609176","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609176","url":null,"abstract":"Neural prostheses could help disabled patients of immobility to restore movements by exploiting brain signals. In the past decade, it has been shown great progress in intracortical neural recording techniques and neural signal processing. We review two classes of methods, population vector and maximal likelihood, for decoding the activity of primary motor cortical neurons in a nonhuman primate during center-out arm reaching movements. In particular, we show that these two methods share the same spirit of pooling activities of population of neurons but with different use of tuning function. We further compare their performance by using real neuronal data from reaching movements and inspect the effects of different parameters. It shows that maximal likelihood approach outperforms the population vector method consistently, which could be due to the more effective use of the tuning function.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198508","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
Optimization of multi-layer artificial neural networks using delta values of hidden layers 基于隐藏层delta值的多层人工神经网络优化
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609169
N. Wagarachchi, S. Karunananda
{"title":"Optimization of multi-layer artificial neural networks using delta values of hidden layers","authors":"N. Wagarachchi, S. Karunananda","doi":"10.1109/CCMB.2013.6609169","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609169","url":null,"abstract":"The number of hidden layers is crucial in multilayer artificial neural networks. In general, generalization power of the solution can be improved by increasing the number of layers. This paper presents a new method to determine the optimal architecture by using a pruning technique. The unimportant neurons are identified by using the delta values of hidden layers. The modified network contains fewer numbers of neurons in network and shows better generalization. Moreover, it has improved the speed relative to the back propagation training. The experiments have been done with number of test problems to verify the effectiveness of new approach.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115143277","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: 14
Control of an unmanned aerial vehicle using a neuronal network 用神经网络控制无人驾驶飞行器
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609168
R. Hercus, Hong-Shim Kong, Kim-Fong Ho
{"title":"Control of an unmanned aerial vehicle using a neuronal network","authors":"R. Hercus, Hong-Shim Kong, Kim-Fong Ho","doi":"10.1109/CCMB.2013.6609168","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609168","url":null,"abstract":"The need for an unmanned aerial vehicle (UAV) controller to operate autonomously and to manage its operations with minimal assistance from humans or rule-based controllers has steadily increased over the years. Numerous approaches have been attempted to address the challenge of developing a UAV with full autonomy. In this paper, a neuronal network-based learning model named NeuraBASE is presented as a possible solution towards autonomy. This neuronal network represents a learning hierarchy of interconnected neurons capable of storing sequences of sensor and motor neuron events. The model is evaluated using experimental scenarios simulated with the STAGE simulation platform, which involves navigational control towards a stationary target. Results show that navigational control with a simple neuronal network can be achieved.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130097550","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: 9
Creation of knowledge & meaning manifested via cortical singularities in cognition: Towards a methodology to understand intentionality and critical behavior in neural correlates of awareness 知识和意义的创造通过认知中的皮质奇点表现出来:走向一种理解意识的神经相关的意向性和关键行为的方法
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609160
J. Davis, R. Kozma
{"title":"Creation of knowledge & meaning manifested via cortical singularities in cognition: Towards a methodology to understand intentionality and critical behavior in neural correlates of awareness","authors":"J. Davis, R. Kozma","doi":"10.1109/CCMB.2013.6609160","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609160","url":null,"abstract":"Experimental and theoretical approaches aiming at the establishment of neural correlates of higher cognitive functions and awareness have been extensively studied in the past decade. Information-theoretical indices are useful tools in establishing quantitative metrics when analyzing data of cognitive experiments. In this work we report a systematic statistical analysis of multiple runs of ECoG measurements over the rabbit visual cortex. The results are interpreted invoking the concept of Pragmatic Information, which is complementary to the Shannon Entropy Index. We interpret these finding based on a dynamical system approach to brains and cognition. We identify large-scale synchronization across broad frequency band as potential manifestation of the `aha' effect, indicating the construction of knowledge and meaning from input sensory data and leading to awareness experience.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128061179","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: 11
A cognitively motivated word sense induction algorithm 一种认知激发的词义归纳算法
2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB) Pub Date : 2013-04-16 DOI: 10.1109/CCMB.2013.6609167
Yair Neuman, Dany H. Assaf, Yohai Cohen
{"title":"A cognitively motivated word sense induction algorithm","authors":"Yair Neuman, Dany H. Assaf, Yohai Cohen","doi":"10.1109/CCMB.2013.6609167","DOIUrl":"https://doi.org/10.1109/CCMB.2013.6609167","url":null,"abstract":"The way in which word senses are produced and identified is of great interest to cognitive sciences as well as to various applications in natural language processing. In this paper, we present a cognitively inspired algorithm of word sense induction. The algorithm fuses the distributional and perceptual information of words. By drawing on minimal resources - word collocations and their level of concreteness/abstractness - our algorithm automatically produces for each target noun a graph that is an endomap with a maximal number of 50 nodes. This graph represents the major senses associated with the noun. Tested on a word sense disambiguation task and on psychological data, our algorithm gains significant empirical support for its efficiency.","PeriodicalId":395025,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115871701","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: 1
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信