One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

A. Novellino

P. D'Angelo

L. Cozzi

M. Chiappalone

V. Sanguineti

S. Martinoia

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

We propose a new multistage procedure for a real-time 
							brain-machine/computer interface (BCI). The developed system allows a BCI user to 
							navigate a small car (or any other object) on the computer screen in real time, in
							 any of the four directions, and to stop it
							 if necessary. Extensive experiments with five young healthy subjects confirmed 
							 the high
							  performance of the proposed 
							 online BCI system. The modular structure, high speed, and the optimal frequency 
							 band characteristics of the BCI platform are features which
							  allow an extension to a substantially higher number of commands in the near future.

Pablo  Martinez

Hovagim  Bakardjian

Andrzej  Cichocki

Fully Online  Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

Controlling a robotic device by using human brain signals is an interesting and challenging task. 
    The device may be complicated to control and the nonstationary nature of the brain signals provides 
    for a rather unstable input. With the use of intelligent processing algorithms adapted to the task 
    at hand, however, the performance can be increased. This paper introduces a shared control system 
    that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. 
    The subject's steering intentions are estimated from electroencephalogram (EEG) signals 
    and passed through to the shared control system before being sent to the wheelchair motors. 
    Experimental results show a possibility for significant improvement in the overall driving performance 
    when using the shared control system compared to driving without it. These results have been 
    obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

Gerolf Vanacker

José del R. Millán

Eileen Lew

Pierre W. Ferrez

Ferran Galán Moles

Johan Philips

Hendrik Van Brussel

Marnix Nuttin

Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

As an emerging technology, brain-computer interfaces (BCIs) bring us 
    new communication interfaces which translate brain activities into control signals for devices
     like computers, robots, and so forth. In this study, we propose a semisupervised support 
     vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at 
     reducing the time-consuming training process. In this algorithm, we apply a semisupervised
      SVM for translating the features extracted from the electrical recordings of brain into control 
      signals. This SVM classifier is built from a small labeled data set and a large unlabeled data 
      set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode
       incremental learning method, which can also be easily applied to the online BCI systems. 
       Additionally, it is suggested in many studies that common spatial pattern (CSP) is very 
       effective in discriminating two different brain states. However, CSP needs a sufficient labeled 
       data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction 
       method for the semisupervised learning algorithm. We apply our
algorithm to two BCI experimental data sets. The offline data analysis results demonstrate 
the effectiveness of our algorithm.

Jianzhao Qin

Yuanqing Li

Wei Sun

A Semisupervised Support Vector Machines Algorithm for BCI Systems

Bivariate statistical modeling from incomplete data is a useful statistical tool that 
					allows to discover the model underlying two data sets when the data in the two sets 
					do not correspond in size nor in ordering. Such situation may occur when the sizes of 
					the two data sets do not match (i.e., there are &#x0093;holes&#x0094; in the data) or when
					 the data sets have been acquired independently. Also, statistical modeling is useful when 
					 the amount of available data is enough to show relevant statistical features of the
					  phenomenon underlying the data. We propose to tackle the problem of statistical 
					  modeling via a neural (nonlinear) system that is able to match its input-output statistic to 
					  the statistic of the available data sets. A key point of the new implementation proposed 
					  here is that it is based on look-up-table (LUT) neural systems, which guarantee a 
					  computationally advantageous way of implementing neural systems. A number of 
					  numerical experiments, performed on both synthetic and real-world data sets, illustrate
					   the features of the proposed modeling procedure.

Simone  Fiori

Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling

Blind separation of the electroencephalogram signals (EEGs) using topographic 
                        independent component analysis (TICA) is an effective tool to group the geometrically 
                        nearby source signals. The TICA algorithm further improves the results if the desired
                         signal sources have particular properties which can be exploited in the separation
                          process as constraints. Here, the spatial-frequency information of the seizure signals
                           is used to design a constrained TICA for the separation of epileptic seizure signal 
                           sources from the multichannel EEGs. The performance is compared with those from 
                           the TICA and other conventional ICA algorithms. The superiority of the new
                            constrained TICA has been validated in terms of signal-to-interference ratio and
                             correlation measurement.

Min Jing

Saeid Sanei

A Novel Constrained Topographic Independent Component Analysis for Separation of Epileptic Seizure Signals

For a robust brain-computer interface (BCI) system based on motor imagery (MI), it 
            should be able to tell when the subject is not concentrating on MI tasks 
            (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, 
            because of the diversity of idle state, detecting idle state without training samples is as 
            important as classifying MI tasks. In this paper, we propose an algorithm for solving this
             problem. A three-class classifier was constructed by combining two two-class classifiers, one 
             specified for idle-state detection and the other for these two MI tasks. Common spatial 
             subspace decomposition (CSSD) was used to extract the features of event-related 
             desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis
              (FDA) was employed in the design of two two-class classifiers for completion of detecting 
              each task, respectively. The algorithm successfully provided a way to solve the problem
               of “idle-state detection without training samples.” The algorithm was applied
                to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was
                 obtained on the testing set. This is the winning algorithm in BCI competition III. In addition,
                  the algorithm was also validated by applying to the EEG data of an MI experiment including
                   “idle” task.

Dan Zhang

Yijun Wang

Xiaorong Gao

Bo Hong

Shangkai Gao

An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

We describe a simple and efficient solution to the problem of reconstructing 
    electromagnetic sources into a canonical or standard anatomical space. Its simplicity rests 
    upon incorporating subject-specific anatomy into the forward model in a way that eschews
     the need for cortical surface extraction. The forward model starts with a canonical cortical
      mesh, defined in a standard stereotactic space. The mesh is warped, in a nonlinear fashion,
       to match the subject's anatomy. This warping is the inverse of the transformation derived 
       from spatial normalization of the subject's structural MRI image, using fully automated
        procedures that have been established for other imaging modalities. Electromagnetic lead 
        fields are computed using the warped mesh, in conjunction with a spherical head model (which 
        does not rely on individual anatomy). The ensuing forward model is inverted using an empirical 
        Bayesian scheme that we have described previously in several publications. Critically, because
         anatomical information enters the forward model, there is no need to spatially normalize the 
         reconstructed source activity. In other words, each source, comprising the mesh, has 
         a predetermined and unique anatomical attribution within standard stereotactic space. 
         This enables the pooling of data from multiple subjects and the reporting of results in 
         stereotactic coordinates. Furthermore, it allows the graceful fusion of fMRI and MEG data 
         within the same anatomical framework.

Jérémie Mattout

Richard N. Henson

Karl J. Friston

Canonical Source Reconstruction for MEG

About this journal

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Chief Editor, Professor Cichocki, engages in world-leading research in the field of artificial intelligence and biomedical applications of advanced data analytics technologies.

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Computational Intelligence and Neuroscience - Table of contents 2007

Computational Intelligence and Neuroscience | Hindawi

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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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Computational Intelligence and Neuroscience

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Computational Intelligence and Neuroscience