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BMI-Training with Body-Internalized FES or Other Wireless and/or Portable Devices

KLINISCHE NEUROPHYSIOLOGIE 2013年第4期44卷 263-267页

作者： Birbaumer, N. Silvoni, S. Ramos, A. Univ Tubingen Inst Med Psychol & Verhaltensneurobiol D-72074 Tubingen Germany Osped San Camillo IRCSS Venice Italy

BMI translates a brain signal to an external device without any motor involvement. BMIs have been used for rehabilitation of chronic stroke in combination with output devices such as functional electrical stimulation (FES), robots and exosceletons as a neuroprosthetic device, and physiotherapy. In addition, neurofeedback and biofeedback (usually using electromyographic (EMG) feedback) shows great promise as a rehabilitation strategy. However, very few adequately controlled studies with large enough patient samples are available, most report proof-of-principle strategies. The combination of BMI with behaviorally oriented physiotherapy to generalize BMI-treatment effects to the home environment proved to be the most efficient non-invasive rehabilitation strategy for severely paralyzed stroke victims. Future directions should test invasive.

关键词： brain-computer-interface neurofeedback neuroplasticity reorganisation neuroprosthesis

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Channel selection and classification of electroencephalogram signals: An artificial neural network and genetic algorithm-based approach

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2012年第2期55卷 117-126页

作者： Yang, Jianhua Singh, Harsimrat Hines, Evor L. Schlaghecken, Friederike Iliescu, Daciana D. Leeson, Mark S. Stocks, Nigel G. Univ Birmingham Sch Biosci Birmingham B15 2TT W Midlands England UCL Dept Comp Sci London WC1E 6BT England Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England Univ Warwick Dept Psychol Coventry CV4 7AL W Midlands England

Objective: An electroencephalogram-based (EEG-based) brain-computer-interface (BCI) provides a new communication channel between the human brain and a computer. Amongst the various available techniques, artificial neural networks (ANNs) are well established in BCI research and have numerous successful applications. However, one of the drawbacks of conventional ANNs is the lack of an explicit input optimization mechanism. In addition, results of ANN learning are usually not easily interpretable. In this paper, we have applied an ANN-based method, the genetic neural mathematic method (GNMM), to two EEG channel selection and classification problems, aiming to address the issues above. Methods and materials: Pre-processing steps include: least-square (LS) approximation to determine the overall signal increase/decrease rate;locally weighted polynomial regression (Loess) and fast Fourier transform (FFT) to smooth the signals to determine the signal strength and variations. The GNMM method consists of three successive steps: (1) a genetic algorithm-based (GA-based) input selection process;(2) multi-layer perceptron-based (MLP-based) modelling;and (3) rule extraction based upon successful training. The fitness function used in the GA is the training error when an MLP is trained for a limited number of epochs. By averaging the appearance of a particular channel in the winning chromosome over several runs, we were able to minimize the error due to randomness and to obtain an energy distribution around the scalp. In the second step, a threshold was used to select a subset of channels to be fed into an MLP, which performed modelling with a large number of iterations, thus fine-tuning the input/output relationship. Upon successful training, neurons in the input layer are divided into four sub-spaces to produce if-then rules (step 3). Two datasets were used as case studies to perform three classifications. The first data were electrocorticography (ECoG) recordings that have been

关键词： Genetic algorithm Artificial neural networks Least square approximation brain-computer-interface EEG channel selection

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BCI control using 4 direction spatial visual attention and real-time fMRI at 7T

BCI control using 4 direction spatial visual attention and r...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Andersson, Patrik Ramsey, Nick F. Pluim, Josien P. W. Viergever, Max A. Univ Med Ctr Utrecht Image Sci Inst Utrecht Netherlands Univ Med Ctr Utrecht Rudolf Magnus Inst Neurosci Dept Neurol & Neurosurg Div Neurosci Utrecht Netherlands

ISBN: (纸本)9781424441242

The goal of brain-computer-interface (BCI) technologies is to "outsource" the muscular control to a computer and create new communication channels, e. g. to people with severe paralysis, by measuring cortical activation changes and linking these changes to commands. Using real-time fMRI at 7T we show that visuospatial attention can be used to reliably regulate cortical activity and that it is possible to separate the cortical responses to multiple attention target regions in real time. The activated regions were first located on the fly using an incremental statistical analysis and the subjects were then given feedback based on the activity in these regions. Visuospatial attention is an attractive addition to the existing BCI control strategies, and the fact that it leaves the motor system still available makes it suitable also for applications aimed for healthy people.

关键词： 4 direction spatial visual attention BCI control biomedical MRI brain-computer interfaces brain-computer-interface communication channels cortical activation changes cortical activity regulation handicapped aids incremental statistical analysis magnetic flux density 7 T medical control systems muscular control neurophysiology paralysis real-time fMRI statistical analysis visuospatial attention

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Acquisition of Logicality in Living Neuronal Networks and its Operation to Fuzzy Bio-Robot System

Acquisition of Logicality in Living Neuronal Networks and it...

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2010 IEEE World Congress on Computational Intelligence

作者： Hayashi, Isao Kiyotoki, Megumi Kiyohara, Ai Tokuda, Minori Kudoh, Suguru N. Kansai Univ Fac Informat Osaka 5691095 Japan Kwansei Gakuin Univ Nishinomiya Hyogo 6691337 Japan Osaka Univ Grad Sch Sci Osaka Japan Kwansei Gakuin Univ Grad Sch Sci Technol Nishinomiya Hyogo 6691337 Japan

ISBN: (纸本)9781424469208

brain-computer-interface has been come into the research limelight. Network dynamics of neurons strongly effects to a control of computer or machine in the outer world. Dissociated culture system with multi-electrode array is useful for elucidation of network dynamics of neurons. We have been investigating action potentials of rat hippocampal neurons cultured on the dish connecting with the outer robot. However, we don't exactly comprehend logicality of living neuronal networks. In this paper, we identify logicality of living neuronal networks with three electrodes in mult-electrode array using fuzzy connective operators consisting of t - norm and t - conorm operators, and we introduce a straight running of fuzzy bio-robot. We concluded that the logicality of living neuronal networks is dynamically changed to weak OR connection from strong AND connection. Additionally, by applying the fuzzy bio-robot to a straight running, we analyzed plasticity of living neuronal network connected to the robot, and we discussed regularity of logical potential response of the neuronal networks.

关键词： biocontrol brain-computer interfaces brain-computer-interface dissociated culture system fuzzy biorobot system fuzzy connective operators fuzzy control living neuronal networks logicality acquisition medical robotics multielectrode array rat hippocampal neurons t-conorm operators t-norm operators

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Stationary Subspace Analysis

Stationary Subspace Analysis

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8th International Conference on Independent Component Analysis and Signal Separation

作者： von Buenau, Paul Meinecke, Frank C. Mueller, Klaus-Robert TU Berlin CS Dept Machine Learning Grp Berlin Germany

ISBN: (纸本)9783642005985

Non-stationarities are an ubiquitous phenomenon in time-series data, yet they pose a challenge to standard methodology: classification models and ICA components, for example, cannot be estimated reliably under distribution changes because the classic assumption of a stationary data generating process is violated. Conversely, understanding the nature of observed non-stationary behaviour often lies at the heart of a scientific question. To this end, we propose a novel unsupervised technique: Stationary Subspace Analysis (SSA). SSA decomposes a multivariate time-series into a stationary and a non-stationary subspace. This factorization is a universal tool for furthering the understanding of non-stationary data. Moreover, we can robustify other methods by restricting them to the stationary subspace. We demonstrate the performance of our novel concept in simulations and present a real world application from brain computer Interfacing.

关键词： Non-Stationarities Source Separation BSS Dimensionality Reduction Covariate Shift brain-computer-interface BCI

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The Potential of the BCI for Accessible and Smart e-Learning

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5th International Conference on Universal Access in Human-computer Interaction held at the HCI International 2009

作者： Adams, Ray Comley, Richard Ghoreyshi, Mahbobeh Middlesex Univ Sch Engn & Informat Sci London NW4 4BT England

ISBN: (纸本)9783642027093

The brain computer interface (BCI) should be the accessibility solution "par excellence" for interactive and e-learning systems. There is a Substantial tradition of research on the human electro encephalogram (EEG) and on BCI systems that are based, inter alia, on EEG measurement. We have not yet seen a viable BCI for e-learning. For many users for a BCI based interface is their first choice for good duality interaction, Such as those with major psychomotor or cognitive impairments. However, there are many more for whom the BCI would be an attractive option given an acceptable learning overhead, including less severe disabilities and safety critical conditions where cognitive overload or limited responses are likely. Recent progress has been modest as there are many technical and accessibility problems to overcome. We present these issues and report a Survey of fifty papers to capture the state-of-the-art in BCI and the implications for e-learning.

关键词： brain-computer-interface e-learning accessibility disability artifacts

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Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2008年第1期18卷 69-78页

作者： Yoo, Seung-Schik Lee, Jong-Hwan O'Leary, Heather Panych, Lawrence P. Jolesz, Ferenc A. Harvard Univ Brigham & Womens Hosp Sch Med Dept Radiol Boston MA 02115 USA

We report the long-term effect of real-time functional MRI (rtfMRI) training on voluntary regulation of the level of activation from a hand motor area. During the performance of a motor imagery task of a right hand, blood-oxygenation-level-dependent (BOLD) signal originating from a primary motor area was presented back to the subject in real-time. Demographically-matched individuals also received the same procedure without valid feedback information. Followed by the initial rtfMRI sessions, both groups underwent 2-week long, daily-practice of the task. Off-line data analysis revealed that the individuals in the experimental group were able to increase the level of BOLD signal from the regulatory target to a greater degree compared with the control group. Furthermore, the learned level of activation was maintained after the 2-week period, with the recruitment of additional neural circuitries such as the hippocampus and the limbo-thalamo-cortical pathway. The activation obtained from the control group, in the absence of proper feedback, was indifferent across the training conditions. The level of BOLD activity from the target regulatory region was positively correlated with a self evaluative score within the experimental group, while the majority of control subjects had difficulty adopting a strategy to attain the desired level of functional regulation. Our results suggest that rtfMRI helped individuals learn how to increase region-specific cortical activity associated with a motor imagery task, and the level of increased activation in motor areas was consolidated after the 2-week self-practice period, with the involvement of neural circuitries implicated in motor skill learning. (c) 2008 Wiley Periodicals, Inc.

关键词： brain-computer-interface biofeedback functional MRI learning and memory

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Atlas-based multichannel monitoring of functional MRI signals in real-time: Automated approach

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HUMAN brain MAPPING 2008年第2期29卷 157-166页

作者： Lee, Jong-Hwan O'Leary, Heather M. Park, Hyunwook Jolesz, Ferenc A. Yoo, Seung-Schik Harvard Univ Sch Med Brigham & Womens Hosp Dept Radiol Boston MA 02115 USA Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Brain Sci Res Ctr Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Biosyst Taejon 305701 South Korea

We report an automated method to simultaneously monitor blood-oxygenation-level-dependent (BOLD) MR signals from multiple cortical areas in real-time. Individual brain anatomy was normalized and registered to a pre-segmented atlas in standardized anatomical space. Subsequently, using real-time fMRI (rtfMRI) data acquisition, localized BOLD signals were measured and displayed from user-selected areas labeled with anatomical and Brodmann's Area (BA) nomenclature. The method was tested on healthy volunteers during the performance of hand motor and internal speech generation tasks employing a trial-based design. Our data normalization and registration algorithm, along with image reconstruction, movement correction and a data display routine were executed with enough processing and communication bandwidth necessary for real-time operation. Task-specific BOLD signals were observed from the hand motor and language areas. One of the study participants was allowed to freely engage in hand clenching tasks, and associated brain activities were detected from the motor-related neural substrates without prior knowledge of the task onset time. The proposed method may be applied to various applications such as neurofeedback, brain-computer-interface, and functional mapping for surgical planning where real-time monitoring of region-specific brain activity is needed. (c) 2007 Wiley-Liss, Inc.

关键词： neurofeedback evoked-potential EEG fMRI brain-computer-interface surgical planning

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On the building of binary spelling interfaces for augmentative communication

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2005年第2期52卷 300-305页

作者： Tregoubov, M Birbaumer, N Accenture GmbH D-61476 Kronberg Germany Univ Trent Fac Phys I-38100 Trento Italy Univ Tubingen Inst Med Psychol & Behav Neurobiol D-72074 Tubingen Germany

A criterion for design of optimal binary spelling interfaces (SI)-the average expectation of number of writing steps (trials) required to write one letter-is presented and discussed. This criterion is relevant for practical usage of any menu-oriented alternative communication system, mechanical device or brain-computer-interface, when a user (typically, a patient with devastating neuromuscular handicap) can not create an intended single binary response with an absolute reliability. An algorithm for building of a corresponding binary tree is developed and evaluated. This algorithm is efficient, when selection probabilities have essentially different values (the worst case).

关键词： binary spelling interface brain-computer-interface

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Threshold adaptation for mean value based operant conditioning

Threshold adaptation for mean value based operant conditioni...

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27th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Ivanova, Galina Perez, Daniel Both, Reinhard Tech Univ Ilmenau Inst Biomed Engn & Informat D-98684 Ilmenau Germany

ISBN: (纸本)0780387406

Biofeedback and a variety of brain-computer-interface methods imply as very first stages the obtainment of control of selected signals e.g. the related generating psychophysiological processes. The basic mechanism in the learning phase is the operant conditioning, which represent a complex behavioral method consisting of several components. One of the most important components is the setting and adjustment of thresholds for the triggering of corresponding rewarding options. An adaptive threshold optimization method, for the training based on average values is presented. The procedure is derivated from the sequential test from Wald. The application of the sequential tests in the learning/training process allows a threshold adaptation corresponding to the abilities of the particular person and to the learning success.

关键词： operant conditioning brain-computer-interface learning sequential methods

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