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Computational Intelligence Magazine, IEEE

Issue 4 • Date November 2009

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Displaying Results 1 - 20 of 20
  • IEEE Computational Intelligence Magazine - cover

    Page(s): c1
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  • Table of contents

    Page(s): 1
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  • When one door closes, another door opens... [Editor's Remarks]

    Page(s): 2
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  • President's farewell message [President's Message]

    Page(s): 3
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  • 2010 IEEE CIS Awards [Society News]

    Page(s): 4 - 8
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  • CIS publication spotlight [Publication Spotlight]

    Page(s): 9
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  • Interview with Incoming Editor-in-Chief of IEEE Transactions on Neural Networks, Derong Liu, Chinese Academy of Sciences and University of Illinois at Chicago [Career Profile]

    Page(s): 10 - 12
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  • Egypt Chapter Report [Family Corner]

    Page(s): 13 - 16
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  • 2009 IEEE Congress on Evolutionary Computation (CEC 2009) [Conference Reports]

    Page(s): 17 - 18
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  • 2009 International Joint Conference on Neural Networks (IJCNN 2009) [Conference Reports]

    Page(s): 18 - 20
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  • 2009 IEEE Symposium Series on Computational Intelligence (SSCI 2009) [Conference Reports]

    Page(s): 20 - 21
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  • VEP-based brain-computer interfaces: time, frequency, and code modulations [Research Frontier]

    Page(s): 22 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (744 KB) |  | HTML iconHTML  

    A brain computer interface (BCI) translates human intentions into control signals to establish a direct communication channel between the human brain and external devices. Because a BCI does not depend on the brain's normal output pathways of peripheral nerves and muscles, it can provide a new communication channel to people with severe motor disabilities. Electroencephalograms (EEGs) recorded from the surface of the scalp are widely used in current BCIs for their non-invasive nature and easy applications. Among EEG based BCIs, systems based on visual evoked potentials (VEPs) have received widespread attention in recent decades. We described the three stimulus modulation approaches used in current VEP based BCIs: time modulation (t-VEP), frequency modulation (f-VEP), and pseudorandom code modulation (c-VEP). We then carried out a detailed comparison of system performance between an f-VEP BCI and a c-VEP BCI. The results show that an f-VEP BCI has the advantage of little or no training and simple system configuration, while the c-VEP based BCI has a higher communication speed. The stimulus modulation design is the crux of VEP based BCI systems. View full abstract»

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  • Analogue evolutionary brain computer interfaces [Application Notes]

    Page(s): 27 - 31
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (701 KB) |  | HTML iconHTML  

    The keyboard is a device that, provides an interface that is reliable but also very unnatural. The mouse is only slightly less primitive, being an electro-mechanical transducer of musculoskeletal movement. Both have been with us for decades, yet they are unusable for people with severe musculoskeletal disorders and are themselves known causes of work-related upper-limb and back disorders, both hugely widespread problems. It will be a major contribution to computer interface technology to replace mouse and keyboard with brain-computer interfaces (BCIs) capable of directly interpreting the desires and intentions of computer users. In this article we describe the approach, results and promising new research directions in the realization of BCIs, with particular reference to a 2D pointing device. Three features characterize the approach. Firstly, BCI is logically analogue, second is the use of evolutionary algorithms, and the third feature is its interdisciplinarity. View full abstract»

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  • Computational intelligent brain computer interaction and its applications on driving cognition

    Page(s): 32 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5784 KB) |  | HTML iconHTML  

    Driving is one of the most common attention demanding tasks in daily life. Driver's fatigue, drowsiness, inattention, and distraction are reported a major causal factor in many traffic accidents. Due to the drivers lost their attention, they had markedly reduced the perception, recognition and vehicle control abilities. In recent years, many computational intelligent technologies were developed for preventing traffic accidents caused by driver's inattention. Driver's drowsiness and distraction related studies had become a major interest research topic in automotive safety engineering. Many researches had investigated the driving cognition in cognitive neuro-engineering, but how to utilize the main findings of driving-related cognitive researches in traditional cognitive neuroscience and integrate with computational intelligence technologies for augmenting driving performance will become a big challenge in the interdisciplinary research area. For this reason, we attempt to integrate the driving cognition for real life application in this study. The implications of the driving cognition in cognitive neuroscience and computational intelligence for daily applications are also demonstrated through two common attention related driving studies: (1) cognitive state monitoring of the driver performing the realistic long-term driving tasks in a simulated realistic driving environment; and (2) to extract the brain dynamic changes of driver's distraction effect during dual task driving. Experimental results of these studies provide new insights into the understanding of complex brain functions of participants actively performing ordinary tasks in natural body positions and situations within real operational environments. View full abstract»

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  • Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces

    Page(s): 47 - 59
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3133 KB) |  | HTML iconHTML  

    Neural networks (NNs) can be deployed in many different ways in signal processing applications. This paper illustrates how neural networks are employed in a prediction based preprocessing framework, referred to as neural-time-series-prediction-preprocessing (NTSPP), in an electroencephalogram (EEG)-based brain-computer interface (BCI). NTSPP has been shown to increase feature separability by mapping the original EEG signals via time-series-prediction to a higher dimensional space. Preliminary results of a similar novel framework are also presented where, instead of using predictive NNs, auto-associative NNs are employed and features are extracted from the output of auto-associative NNs trained to specialize on EEG signals for particular brain states. The results show that this preprocessing framework referred to as auto-associative NN preprocessing (ANNP) also has the potential to improve the performance of BCIs. Both the NTSPP and ANNP are compared with and deployed in conjunction with the well know common spatial patterns (CSP) to produce a BCI system which significantly outperforms either approach operating independently and has the potential to produce good performances even with a lower number of EEG channels compared to a multichannel BCI. Multichannel BCIs normally perform better that 2-3 channel BCIs however reducing the number of EEG channels required can positively impact on the time needed to mount electrodes and minimize the obtrusiveness of the electrode montage for the user. It is also shown that NTSPP can improve the potential for employing existing BCI methods with minimal subject-specific parameter tuning to deploy the BCI autonomously. Results are presented with six different classification approaches including various statistical classifiers such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and a Bayes classifier. View full abstract»

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  • Computational Intelligence in Bioinformatics (Fogel, G.B. et al; 2008) [Book review]

    Page(s): 60 - 61
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  • IEEE Transactions on Evolutionary Computation: Special Issue on Advances in Memetic Computation

    Page(s): 62
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  • Conference calendar

    Page(s): 63
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  • 2009 Index IEEE Computational Intelligence Magazine Vol. 4

    Page(s): 1 - 4
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  • 2010 IEEE World Congress on Computational Intelligence - Call for papers

    Page(s): c3
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope