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Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011 IEEE Symposium on

Date 11-15 April 2011

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  • [Front cover]

    Page(s): c1
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  • [Copyright notice]

    Page(s): 1
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  • Table of contents

    Page(s): iii - vii
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  • IEEE CCMB 2011 Committee

    Page(s): viii
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  • Performance analysis of left/right hand movement classification from EEG signal by intelligent algorithms

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (426 KB) |  | HTML iconHTML  

    Brain Computer interfaces (BCI) has immense potentials to improve human lifestyle including that of the disabled. BCI has possible applications in the next generation human-computer, human-robot and prosthetic/assistive devices for rehabilitation. The dataset used for this study has been obtained from the BCI competition-II 2003 databank provided by the University of Technology, Graz. After pre-processing of the signals from their electrodes (C3 & C4), the wavelet coefficients, Power Spectral Density of the alpha and the central beta band and the average power of the respective bands have been employed as features for classification. This paper presents a comparative study of different classification methods including linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), k-nearest neighbor (KNN) algorithm, linear support vector machine (SVM), radial basis function (RBF) SVM and naive Bayesian classifiers algorithms in differentiating the raw EEG data obtained, into their associative left/right hand movements. Performance of left/right hand classification is studied using both original features and reduced features. The feature reduction here has been performed using Principal component Analysis (PCA). It is as observed that RBF kernelised SVM classifier indicates the highest performance accuracy of 82.14% with both original and reduced feature set. However, experimental results further envisage that all the other classification techniques provide better classification accuracy for reduced data set in comparison to the original data. It is also noted that the KNN classifier improves the classification accuracy by 5% when reduced features are used instead of the original. View full abstract»

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  • Storing objects in a short-term biologically inspired memory system for artificial agents

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (251 KB) |  | HTML iconHTML  

    In this paper we discuss a method for the short-term storage of objects that an artificial agent encounters in a simulated environment. We examine how short-term storage is undertaken in biological systems, as well as current research theories in this area. We then propose a method for emulating some aspects of biological memory systems in an artificial agent and examine its limitations. View full abstract»

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  • Implementing a cognition cycle with words computation

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB) |  | HTML iconHTML  

    Implementing a cognition cycle provides a real impression of the mechanisms of a natural intelligence system for explaining interdependent information processing activities among cognitive processes of the brain. The nature of information processing in the human brain is fuzzy. In this article, the Motivation/Attitude Driven Behavior (MADB) model as a kind of a cognition cycle is developed according to the fuzzy sets theory, a psychological model for managing new information is proposed, and applications of the behavioral models in computer engineering and especially computational intelligence are introduced and discussed. Subsequently, the transmissions of information in the MADB model are simulated toward implementing an intelligent humanoid behavior mechanism, and, finally, the results of the simulation are analyzed. View full abstract»

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  • Modeling decisions by brains that think, feel, and vegetate

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (242 KB) |  | HTML iconHTML  

    This paper summarizes three interrelated neural network models of data on emotionally influenced decision making: the first on a gambling task, the second on probability judgment, and the third on probability weighting. The networks incorporate data on executive regions of the brain and organizing principles such as adaptive resonance and fuzzy traces that have been utilized to model other cognitive data. View full abstract»

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  • Deciding in uncertainy: The creativity effects

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (133 KB) |  | HTML iconHTML  

    As part of the analysis of decision making with and without attention, we introduce the notion of the `Creativity Effects', and indicate how they may be used in certain paradigms to explain how apparently attention-free decisions arise through a process in which attention and the resulting consciousness of a target stimulus play a crucial role. View full abstract»

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  • Uncertainty modeling for spatial data fusion and mining

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB) |  | HTML iconHTML  

    Fusion and mining of uncertain heterogeneous spatial data in the cyber-physical space are challenging problems especially to deal in a coordinated way with both topological and geometrical uncertainties. This paper explores opportunities to meet these challenges by generalizing the Dynamic Logic of Phenomena (DLP) and the Neural Modeling Field (NMF) Theory for geo-spatial data. The main idea behind success of NMF and DLP in applications is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model with data. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process mimics processes of the mind and natural evolution. This paper also outlines a generalization of k-nearest neighbors' algorithm in the DLP framework for geo-spatial structural data mining and fusion. The generalization is demonstrated on the problem of vector to raster conflation (VRC) of topologically and geometrically uncertain geo-spatial data. In this problem, multiple sources of data uncertainty include feature extraction from satellite imagery, vectorization of extracted features, and the whole process of generation of vector layers such as road and drainage networks. The derived conflation algorithm exploits generalized DLP with the lattice of models based on the hierarchy of topological and geometric uncertainties. The efficiency of this algorithm is shown on conflating satellite imagery with Tiger vector data. View full abstract»

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  • The performance of a linear learning algorithm for cross-situational vocabulary learning

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (166 KB) |  | HTML iconHTML  

    Cross-situational learning is based on the idea that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we present the results of an extensive statistical analysis of the performance of a linear learning algorithm for learning a one-to-one mapping between N objects and N words based solely on the co-occurrence between objects and words. In particular, a learning trial in our cross-situational learning scenario consists of the presentation of C <; N objects together with a word, which refers to one of the objects in the context. We find that the learning error ϵ decreases exponentially as the number of learning trials T increases, i.e., ϵ ~ exp (-αT) where the learning rate is given by α = (N-C) / [N (N-1)]. View full abstract»

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  • Mirror neurons, language, and embodied cognition

    Page(s): 1 - 7
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    Basic mechanisms of the mind, cognition, language, its semantic and emotional mechanisms are modeled using dynamic logic (DL). This cognitively and mathematically motivated model leads to a dual-model hypothesis of language and cognition. This models joint emergence of language and cognition from mirror neuron system. View full abstract»

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  • A simple and efficient way to store many messages using neural cliques

    Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (201 KB) |  | HTML iconHTML  

    Associative memories are devices that are able to learn messages and to recall them in presence of errors or erasures. Their mechanics is similar to that of error correcting decoders. However, the role of correlation is opposed in the two devices, used as the essence of the retrieval process in the first one and avoided in the latter. In this paper, original codes are introduced to allow the effective combination of the two domains. The main idea is to associate a clique in a binary neural network with each message to learn. The obtained performance is dramatically better than that given by the state of the art, for instance Hopfield Neural Networks. Moreover, the model proposed is biologically plausible; it uses sparse binary connections between clusters of neurons provided with only two operations: sum and selection of maximum. View full abstract»

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  • Knowledge extraction from a class of support vector machines using the fuzzy all-permutations rule-base

    Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1251 KB) |  | HTML iconHTML  

    Support vector machines (SVMs) proved to be highly efficient in various classification tasks. However, the knowledge learned by the SVM is encoded in a long list of parameter values and it is not easy to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy-rule base, the fuzzy all-permutations rule base (FARB). This equivalence can be used to provide a symbolic representation of the SVM functioning. This leads to a new approach for knowledge extraction from SVMs. Two simple examples demonstrate the effectiveness of this approach. View full abstract»

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  • Confabulation based sentence completion for machine reading

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (358 KB) |  | HTML iconHTML  

    Sentence completion and prediction refers to the capability of filling missing words in any incomplete sentences. It is one of the keys to reading comprehension, thus making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics the human information processing. The building of confabulation knowledge base uses an unsupervised machine learning algorithm that extracts the relations between objects at the symbolic level. In this work, we propose performance improved training and recall algorithms that apply the cogent confabulation model to solve the sentence completion problem. Our training algorithm adopts a two-level hash table, which significantly improves the training speed, so that a large knowledge base can be built at relatively low computation cost. The proposed recall function fills missing words based on the sentence context. Experimental results show that our software can complete trained sentences with 100% accuracy. It also gives semantically correct answers to more than two thirds of the testing sentences that have not been trained before. View full abstract»

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  • Dynamic neural field optimization using the unscented Kalman filter

    Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (367 KB) |  | HTML iconHTML  

    Dynamic neural fields have been proposed as a continuous model of a neural tissue. When dynamic neural fields are used in practical applications, the tuning of their parameters is a challenging issue that most of the time relies on expert knowledge on the influence of each parameter. The methods that have been proposed so far for automatically tuning these parameters rely either on genetic algorithms or on gradient descent. The second category of methods requires to explicitly compute the gradient of a cost function which is not always possible or at least difficult and costly. Here we propose to use unscented Kalman filters, a derivative-free algorithm for parameter estimation, which reveals to efficiently optimize the parameters of a dynamic neural field. View full abstract»

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  • What to measure next to improve decision making? On top-down task driven feature saliency

    Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (290 KB) |  | HTML iconHTML  

    Top-down attention is modeled as decision making based on incomplete information. We consider decisions made in a sequential measurement situation where initially only an incomplete input feature vector is available, however, where we are given the possibility to acquire additional input values among the missing features. The procecure thus poses the question what to do next? We take an information theoretical approach implemented for generality in a generative mixture model. The framework allows us reduce the decision about what to measure next in a classification problem to the estimation of a few one-dimensional integrals per missing feature. We demonstrate the viability of the framework on four well-known classification problems. View full abstract»

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  • EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game

    Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1032 KB) |  | HTML iconHTML  

    This paper presents an overview of a multistage signal processing framework to tackle the main challenges in continuous control protocols for motor imagery based synchronous and self-paced BCIs. The BCI can be setup rapidly and automatically even when conducting an extensive search for subject-specific parameters. A new BCI-based game training paradigm which enables assessment of continuous control performance is also introduced. A range of offline results and online analysis of the new game illustrate the potential for the proposed BCI and the advantages of using the game as a BCI training paradigm. View full abstract»

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  • Comparative study of band-power extraction techniques for Motor Imagery classification

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (205 KB) |  | HTML iconHTML  

    We review different techniques for extracting the power information contained in frequency bands in the context of electroencephalography (EEG) based Brain-Computer Interfaces (BCI). In this domain it is common to apply only one algorithm for extracting the power information. However previous work and our current study confirm that one may indeed expect varying degrees of success by choosing inadequate algorithms for the power extraction. Our results suggest that on average one algorithm seems superior for extracting the power information for Motor Imagery tasks : the application of a Morlet wavelet on the raw EEG signals, with the time-frequency resolution tradeoff selected by cross-validation. View full abstract»

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  • Composite Filter Bank Common Spatial Pattern for motor imagery-based Brain-Computer Interface

    Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (510 KB) |  | HTML iconHTML  

    The Filter Bank Common Spatial Pattern (FBCSP) algorithm employs multiple spatial filters across a bank of band-pass filtered EEC using the CSP algorithm. This is as opposed to the commonly used single spatial filter from band-pass filtered EEC. Hence, the FBCSP yields improved performance in autonomous selection of key temporal-spatial discriminative EEC characteristics in motor imagery-based Brain-Computer Interfaces (MI-BCI). However, the multiple spatial filtering involves multiple estimations of covariance matrices across the different frequency bands. Thus, the use of multiple spatial filters increases the sensitivity of the FBCSP algorithm to noise, artifacts and outliers compared to the CSP algorithm. Furthermore, the multiple spatial patterns are also less interpretable than a single spatial pattern. Hence this paper proposes a Composite FBCSP algorithm that employs a single spatial filter instead of multiple spatial filters. The composite spatial filter is computed from a weighted sum of covariance matrices whereby the weights are determined from the mutual information across selected frequency band. The performance of the Composite FBCSP is compared to the FBCSP on a publicly available dataset and data collected from 5 healthy subjects using session-to-session transfer kappa values on the independent test data. The results revealed improvements in accuracy and interpretability in the spatial patterns. View full abstract»

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  • Looking around with your brain in a virtual world

    Page(s): 1 - 8
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    Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This real-time system and a game application in which these pipelines are to be used were implemented. The game is set in a virtual environment where player is a wildlife photographer on an uninhabited island. Overt attention is used to adjust the angle of the first person camera, when the player is tracking animals. When making a photograph, the animal will flee when it notices it is looked at directly, so covert attention is required to get a good shot. Future work will entail user tests with this system to evaluate usability, user experience, and characteristics of the signals related to overt and covert attention when used in such an immersive environment. View full abstract»

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  • Accuracy of a P300 speller for people with motor impairments

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (685 KB) |  | HTML iconHTML  

    A Brain-Computer Interface (BCI) provides a completely new output pathway and so an additional possible way a person can express himself if he/she suffers disorders like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury or other diseases which impair the function of the common output pathways which are responsible for the control of muscles or impair the muscles. Although most BCIs are thought to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on 10 physically disabled people to the results of a previous study, taken of 100 healthy persons. We prove that, under certain constraints most patients are able to control a P300-based spelling device with almost the same accuracy than the healthy ones. Tuning parameters are discussed as well as criteria for people who are not able to use this device. View full abstract»

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  • Self-paced brain-controlled wheelchair methodology with shared and automated assistive control

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB) |  | HTML iconHTML  

    The consistency and reliability of the brain computer interface (BCI) system is often questioned to be safe for controlling a wheelchair as BCIs characteristically experience a low signal-to-noise ratio and low classification accuracy. Electroencephalogram (EEG) acquired non-invasively consists of multiple time-series which are highly correlated because of volume conduction and ambient noises, thus providing a rather blurred image of the brain activity. This low signal-to-noise ratio and low spatial resolution of the data can degrade the translational performance of the BCI. To overcome the low classification accuracy and the uncertainty in commands of the BCI systems, the user has to impart additional concentration and time to navigate the wheelchair to the desired location. This paper presents a brain-controlled wheelchair (BCW) control strategy that reduces the total time required to complete a task and the concentration effort imparted by the user. Two BCW approaches are investigated in this work; a synchronous BCW and a self-paced BCW. These methodologies involve a shared control methodology between the BCI/user component and the automated assistive control (AAC) component. The proposed BCW strategies are compared to state-of-the-art BCW control methodologies available in the literature. The results show that the proposed methods not only reduce the concentration time but also provide a safer and reliable control compared to other BCWs. View full abstract»

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  • The application of a real-time rapid-prototyping environment for the behavioral rehabilitation of a lost brain function in rats

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (354 KB) |  | HTML iconHTML  

    In this paper we propose a Rapid Prototyping Environment (RPE) for real-time biosignal analysis including ECG, EEG, ECoG and EMG of humans and animals requiring a very precise time resolution. Based on the previous RPE which was mainly designed for developing Brain Computer Interfaces (BCI), the present solution offers tools for data preprocessing, analysis and visualization even in the case of high sampling rates and furthermore tools for precise cognitive stimulation. One application of the system, the analysis of multi-unit activity measured from the brain of a rat is presented to prove the efficiency of the proposed environment. The experimental setup was used to design and implement a biomimetic, biohybrid model for demonstrating the recovery of a learning function lost with age. Throughout the paper we discuss the components of the setup, the software structure and the online visualization. At the end we present results of a real-time experiment in which the model of the brain learned to react to the acquired signals. View full abstract»

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  • Control of a lower limb active prosthesis with eye movement sequences

    Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (390 KB) |  | HTML iconHTML  

    Current active leg prostheses do not integrate the most recent advances in Brain-Computer Interfaces (BCI) and bipedal robotics. Moreover, their actuators are seldom driven by the subject's intention. In this paper, we propose an original and biologically-inspired leg prosthesis control scheme, which brings together these three aspects. It is composed of an EOG-based eye tracker and a Programmable Central Pattern Generator (PCPG). In a first step, specific sequences of eye movements executed by the user are identified by the eye tracking system. These sequences are then converted to high-level commands (such as accelerate, decelerate or stop) and sent to the prosthesis actuator control unit. In this unit, a PCPG is implemented, which is able to model human walk in a perfectly periodic way. One of the main interests of that tool is the possibility to modify the gait pattern to adapt to different walking speeds in a smooth way. Several results from previous studies are summarized and discussed in order to demonstrate the feasibility of such a system. View full abstract»

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