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Toward Brain-Computer Interfacing

Cover Image Copyright Year: 2007
Author(s): Dornhege, G.; del R. Millán, J.; Hinterberger, T.; McFarland, D.; Müller, K.
Publisher: MIT Press
Content Type : Books & eBooks
Topics: Bioengineering
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Abstract

Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin.

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      Front Matter

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): i - xii
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Half Title, Neural Information Processing Series, Title, Copyright, Contents, Foreword, Preface View full abstract»

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      An Introduction to Brain-Computer Interfacing

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 1 - 25
      Copyright Year: 2007

      MIT Press eBook Chapters

      We provide a compact overview of invasive and noninvasive brain-computer interfaces (BCI). This serves as a high-level introduction to an exciting and active field and sets the scene for the following sections of this book. In particular, the chapter briefly assembles information on recording methods and introduces the physiological signals that are being used in BCI paradigms. Furthermore, we review the spectrum from subject training to machine learning approaches. We expand on clinical and human-machine interface (HMI) applications for BCI and discuss future directions and open challenges in the BCI field. View full abstract»

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      BCI Systems and Approaches

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 27 - 30
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: Introduction View full abstract»

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      Noninvasive Brain-Computer Interface Research at the Wadsworth Center

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 31 - 42
      Copyright Year: 2007

      MIT Press eBook Chapters

      The primary goal of the Wadsworth Center brain-computer interface (BCI) program is to develop electroencephalographic (EEG) BCI systems that can provide severely disabled individuals with an alternative means of communication and/or control. We have shown that people with or without motor disabilities can learn to control sensorimotor rhythms recorded from the scalp to move a computer cursor in one or two dimensions and we have also used the P300 event-related potential as a control signal to make discrete selections. Overall, our research indicates there are several approaches that may provide alternatives for individuals with severe motor disabilities. We are now evaluating the practicality and effectiveness of a BCI communication system for daily use by such individuals in their homes. View full abstract»

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      Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 43 - 64
      Copyright Year: 2007

      MIT Press eBook Chapters

      An overview of different approaches to brain-computer interfaces (BCIs) developed in our laboratory is given. An important clinical application of BCIs is to enable communication or environmental control in severely paralyzed patients. The BCI “Thought-Translation Device (TTD)” allows verbal communication through the voluntary self-regulation of brain signals (e.g., slow cortical potentials (SCPs)), which is achieved by operant feedback training. Humans' ability to self-regulate their SCPs is used to move a cursor toward a target that contains a selectable letter set. Two different approaches were followed to developWeb browsers that could be controlled with binary brain responses. Implementing more powerful classification methods including different signal parameters such as oscillatory features improved our BCI considerably. It was also tested on signals with implanted electrodes. Most BCIs provide the user with a visual feedback interface. Visually impaired patients require an auditory feedback mode. A procedure using auditory (sonified) feedback of multiple EEG parameters was evaluated. Properties of the auditory systems are reported and the results of two experiments with auditory feedback are presented. Clinical data of eight ALS patients demonstrated that all patients were able to acquire efficient brain control of one of the three available BCI systems (SCP, µ-rhythm, and P300), most of them used the SCP-BCI. A controlled comparison of the three systems in a group of ALS patients, however, showed that P300-BCI and the µ-BCI are faster and more easily acquired than SCP-BCI, at least in patients with some rudimentary motor control left. Six patients who started BCI training after entering the completely locked-in state did not achieve reliable communication skills with any BCI system. One completely locked-in patient was able t o communicate shortly with a ph-meter, but lost control afterward. View full abstract»

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      Graz-Brain-Computer Interface: State of Research

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 65 - 84
      Copyright Year: 2007

      MIT Press eBook Chapters

      A brain-computer interface (BCI) transforms signals originating from the human brain into commands that can control devices or applications. In this way, a BCI provides a new nonmuscular communication channel and control technology for those with severe neuromuscular disorders. The immediate goal is to provide these users, who may be completely paralyzed, or “locked in,” with basic communication capabilities so they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. The Graz-BCI system uses electroencephalographic (EEG) signals associated with motor imagery, such as oscillations of β or µ rhythms or visual and somatosensory steady-state evoked potentials (SSVEP, SSSEP) as input signal. Special effort is directed to the type of motor imagery (kinesthetic or visual-motor imagery), the use of complex band power features, the selection of important features, and the use of phase-coupling and adaptive autoregressive parameter estimation to improve single-trial classification. A new approach is also the use of steady-state somatosensory evoked potentials to establish a communication with the help of tactile stimuli. In addition, different Graz-BCI applications are reported: control of neuroprostheses, control of a spelling system, and first steps toward an asynchronous (uncued) BCI for navigation in a virtual environment. View full abstract»

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      The Berlin Brain-Computer Interface: Machine Learning-Based Detection of User Specific Brain States

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 85 - 102
      Copyright Year: 2007

      MIT Press eBook Chapters

      The Berlin Brain-Computer Interface (BBCI) project develops an EEG-based BCI system that uses machine learning techniques to adapt to the specific brain signatures of each user. This concept allows to achieve high quality feedback already in the very first session without subject training. Here we present the broad range of investigations and experiments that have been performed within the BBCI project. The first kind of experiments analyzes the predictability of performing limbs from the premovement (readiness) potentials including successful feedback experiments. The limits with respect to the spatial resolution of the somatotopy are explored by contrasting brain patterns of movements of (1) left vs. right foot, (2) index vs. little finger within one hand, and (3) finger vs. wrist vs. elbow vs. shoulder within one arm. A study of phantom movements of patients with traumatic amputations shows the potential applicability of this BCI approach. In a complementary approach, voluntary modulations of sensorimotor rhythms caused by motor imagery (left hand vs. right hand vs. foot) are translated into a proportional feedback signal. We report results of a recent feedback study with six healthy subjects with no or very little experience with BCI control: Half of the subjects achieved an information transfer rate above 35 bits per minute (bpm). Furthermore, one subject used the BBCI to operate a mental typewriter in free spelling mode. The overall spelling speed was 4.5 letters per minute including the time needed for the correction errors. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials. View full abstract»

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      The IDIAP Brain-Computer Interface: An Asynchronous Multiclass Approach

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 103 - 110
      Copyright Year: 2007

      MIT Press eBook Chapters

      In this chapter, we give an overview of our work on a self-paced asynchronous BCI that responds every 0.5 seconds. A statistical Gaussian classifier tries to recognize three different mental tasks; it may also respond “unknown” for uncertain samples as the classifier incorporates statistical rejection criteria. We report our experience with different subjects. We also describe three brain-actuated applications we have developed: a virtual keyboard, a brain game, and a mobile robot (emulating a motorized wheelchair). Finally, we discuss current research directions we are pursuing to improve the performance and robustness of our BCI system, especially for real-time control of brain-actuated robots. View full abstract»

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      Brain Interface Design for Asynchronous Control

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 111 - 121
      Copyright Year: 2007

      MIT Press eBook Chapters

      The concept of self-paced control has recently emerged from within the general field of brain-computer interface research. The use of assistive devices in real-world environments is best served by interfaces operated in an asynchronous manner. This self-paced or asynchronous mode of device control is more natural than the more commonly studied synchronized control mode whereby the system dictates the control of the user. The Neil Squire Society develops asynchronous, direct brain-switches for self-paced control applications. Our latest switch design operated with a mean activation rate of 73 percent and false positive error rates of 2 percent. This report provides an introduction to asynchronous control, summarizes our results to date, and details some key issues that specifically relate to brain interface design for asynchronous control. View full abstract»

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      Invasive BCI Approaches

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 123 - 127
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: Introduction View full abstract»

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      Electrocorticogram as a Brain-Computer Interface Signal Source

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 129 - 145
      Copyright Year: 2007

      MIT Press eBook Chapters

      The use of electrocorticogram (ECoG) as the signal source for brain-computer interfaces (BCIs) has advantages for both the potential BCI user and the BCI researcher. However, research using ECoG can be logistically challenging. Visualization of time- and frequencybased characteristics of movement-related potentials in ECoG illustrates the features available for detection by a BCI and their spatial distribution. A quantitative comparison of the detection possible with EEG and ECoG verifies the signal quality advantages of ECoG and the utility of spatial filtering for improving detection. A quadratic detector based on a twocovariance signal model is presented as the basis for a BCI using ECoG, and the detection achieved by the quadratic detector is compared to BCI methods based on cross-correlation and bandpower. The quadratic detector provides dramatically improved detection and response time over the cross-correlation-based method. View full abstract»

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      Probabilistically Modeling and Decoding Neural Population Activity in Motor Cortex

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 147 - 159
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter introduces and summarizes recent work on probabilistic models of motor cortical activity and methods for inferring, or decoding, hand movements from this activity. A simple generalization of previous encoding models is presented in which neural firing rates are represented as a linear function of hand movements. A Bayesian approach is taken to exploit this generative model of firing rates for the purpose of inferring hand kinematics. In particular, we consider approximations of the encoding problem that allow efficient inference of hand movement using a Kalman filter. Decoding results are presented and the use of these methods for neural prosthetic cursor control is discussed. View full abstract»

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      The Importance of Online Error Correction and Feed-Forward Adjustments in Brain-Machine Interfaces for Restoration of Movement

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 161 - 173
      Copyright Year: 2007

      MIT Press eBook Chapters

      Intended movement can now be decoded in real time from neural activity recorded via intracortical microelectrodes implanted in motor areas of the brain. This opens up the possibility that severely paralyzed individuals may be able to use their extracted movement commands to control various assistive devices directly. Even direct control of one's own paralyzed limbs may be possible by combining brain recording and decoding technologies with functional electrical stimulation systems that generate movement in paralyzed limbs by applying low levels of current to the peripheral nerves. However, the microelectrode arrays can record only a small fraction of the neurons that normally are used to control movement, and we are unable to decode the user's desired movement without errors. This chapter discusses experiments in which a monkey used its cortical signals to control the movements of a 3D cursor and a robotic arm in real time. Both consistent errors and random errors were seen when decoding intended movement. However, the animal learned to compensate for consistent decoding errors by making feed-forward adjustments to its motor plan. The animal also learned to compensate for random decoding errors by using visual feedback to make online error corrections to the evolving movement trajectories. This ability to compensate for imperfect decoding suggests intracortical signals may be quite useful for assistive device control even if the current technology does not perfectly extract the users native movement commands. View full abstract»

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      Advances in Cognitive Neural Prosthesis: Recognition of Neural Data with an Information-Theoretic Objective

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 175 - 190
      Copyright Year: 2007

      MIT Press eBook Chapters

      We give an overview of recent advances in cognitive-based neural prostheses, and point out the major differences with respect to commonly used motor-based brain-machine interfaces. While encouraging results in neuroprosthetic research have demonstrated the proof of concept, the development of practical neural prostheses is still in the phase of infancy. To address complex issues arising in the development of practical neural prostheses we review several related studies ranging from the identification of new cognitive variables to the development of novel signal processing tools. In the second part of this chapter, we discuss an information-theoretic approach to the extraction of low-dimensional features from high-dimensional neural data. We argue that this approach may be better suited for certain neuroprosthetic applications than the traditionally used features. An extensive analysis of electrical recordings from the human brain demonstrates that processing data in this manner yields more informative features than off-the-shelf techniques such as linear discriminant analysis. Finally, we show that the feature extraction is not only a useful dimensionality reduction technique, but also that the recognition of neural data may improve in the feature domain. View full abstract»

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      A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 191 - 201
      Copyright Year: 2007

      MIT Press eBook Chapters

      We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system's input are the instantaneous spike rates. The system's state dynamics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity-to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajectories on the basis of neural activity in the motor cortex of behaving monkeys and find that the proposed approach is more accurate than a static approach based on support vector regression and the Kalman filter. View full abstract»

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      BCI Techniques

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 203 - 206
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: Introduction View full abstract»

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      General Signal Processing and Machine Learning Tools for BCI Analysis

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 207 - 233
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter discusses signal processing and machine learning techniques and their application to brain-computer interfacing. A broader overview of the general signal processing and classification methods as used in single-trial EEG analysis is given. For more specialized algorithms, the reader is referred to the original publications. Furthermore, validation techniques and robustification are discussed briefly. View full abstract»

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      Classifying Event-Related Desynchronization in EEG, ECoG, and MEG Signals

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 235 - 259
      Copyright Year: 2007

      MIT Press eBook Chapters

      We present the results from three motor imagery-based brain-computer interface experiments. Brain signals were recorded from eight untrained subjects using EEG, four using ECoG, and ten using MEG. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in the clinical application of a BCI, so we aim to obtain the best possible classification performance in a short time. Accordingly, the burden of adaptation is on the side of the computer rather than the user, so we must adopt a machine learning approach to the analysis. We introduce the required machine-learning vocabulary and concepts, and then present quantitative results that focus on two main issues. The first is the effect of the number of trials—how long does the recording session need to be? We find that good performance could be achieved, on average, after the first 200 trials in EEG, 75–100 trials in MEG, and 25–50 trials in ECoG. The second issue is the effect of spatial filtering—we compare the performance of the original sensor signals with that of the outputs of independent component Analysis and the common spatial pattern algorithm, in each of the three sensor types. We find that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. The unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm in all cases—the latter suffered from poor generalization performance due to overfitting in ECoG and MEG, although this could be alleviated by reducing the number of sensors used as input to the algorithm. View full abstract»

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      Classification of Time-Embedded EEG Using Short-Time Principal Component Analysis

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 261 - 277
      Copyright Year: 2007

      MIT Press eBook Chapters

      Principal component analysis (PCA) is often used to project high-dimensional signals to lower dimensional subspaces defined by basis vectors that maximize the variance of the projected signals. The projected values can be used as features for classification problems. Data containing variations of relatively short duration and small magnitude, such as those seen in EEG signals, may not be captured by PCA when applied to time series of long duration. Instead, PCA can be applied independently to short segments of data and the basis vectors themselves can be used as features for classification. Here this is called the short-time principal component analysis (STPCA). In addition, the time-embedding of EEG samples is investigated prior to STPCA, resulting in a representation that captures EEG variations in space and time. The resulting features of the analysis are then classified via a standard linear discriminant analysis (LDA). Results are shown for two datasets of EEG, one recorded from subjects performing five mental tasks, and one from the third BCI Competition recorded from subjects performing one mental task and two imagined movement tasks. View full abstract»

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      Noninvasive Estimates of Local Field Potentials for Brain-Computer Interfaces

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 279 - 290
      Copyright Year: 2007

      MIT Press eBook Chapters

      Recent experiments have shown the possibility of using the brain electrical activity to directly control the movement of robots or prosthetic devices in real time. Such neuroprostheses can be invasive or noninvasive, depending on how the brain signals are recorded. In principle, invasive approaches will provide a more natural and flexible control of neuroprostheses, but their use in humans is debatable given the inherent medical risks. Noninvasive approaches mainly use scalp electroencephalogram (EEG) signals and their main disadvantage is that these signals represent the noisy spatiotemporal overlapping of activity arising from very diverse brain regions, that is, a single scalp electrode picks up and mixes the temporal activity of myriad neurons at very different brain areas. To combine the benefits of both approaches, we propose to rely on the noninvasive estimation of local field potentials (eLFP) in the whole human brain from the scalp-measured EEG data using a recently developed inverse solution (ELECTRA) to the EEG inverse problem. The goal of a linear inverse procedure is to deconvolve or unmix the scalp signals attributing to each brain area its own temporal activity. To illustrate the advantage of this approach, we compare, using identical sets of spectral features, classification of rapid voluntary finger self-tapping with left and right hands based on scalp EEG and eLFP on three subjects using different numbers of electrodes. It is shown that the eLFP-based Gaussian classifier outperforms the EEG-based Gaussian classifier for the three subjects. View full abstract»

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      Error-Related EEG Potentials in Brain-Computer Interfaces

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 291 - 301
      Copyright Year: 2007

      MIT Press eBook Chapters

      Brain-computer interfaces (BCI), as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech, and gestures), are prone to errors in the recognition of subject's intent. An elegant approach to improve the accuracy of BCIs consists in a verification procedure directly based on the presence of error-related potentials (ErrP) in the EEG recorded right after the occurrence of an error. Most of these studies show the presence of ErrP in typical choice reaction tasks where subjects respond to a stimulus and ErrP arise following errors due to the subject's incorrect motor action. However, in the context of a BCI, the central question is: Are ErrP also elicited when the error is made by the interface during the recognition of the subject's intent? We have thus explored whether ErrP also follow a feedback indicating incorrect responses of the interface and no longer errors of the subjects themselves. Four healthy volunteer subjects participated in a simple human-robot interaction experiment (i.e., bringing the robot to either the left or right side of a room), which seemed to reveal a new kind of ErrP. These “interaction ErrP” exhibit a first sharp negative peak followed by a broader positive peak and a second negative peak (∼270, ∼400, and ∼550 ms after the feedback, respectively). But to exploit these ErrP, we need to detect them in each single trial using a short window following the feedback that shows the response of the classifier embedded in the BCI. We have achieved an average recognition rate of correct and erroneous single trials of 83.7 percent and 80.2 percent, respectively.We also show that the integration of these ErrP in a BCI, where the subject's intent is not executed if an ErrP is detected, significantly improves the performance of the BCI. View full abstract»

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      Adaptation in Brain-Computer Interfaces

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 303 - 325
      Copyright Year: 2007

      MIT Press eBook Chapters

      One major challenge in brain-computer interface (BCI) research is to cope with the inherent nonstationarity of the recorded brain signals caused by changes in the subject's brain processes during an experiment. Online adaptation of the classifier embedded in the BCI is a possible way of tackling this issue. In this chapter, we investigate the effect of adaptation on the performance of the classifier embedded in three different BCI systems, all of them based on noninvasive electroencephalogram (EEG) signals. Through this adaptation we aim to keep the classifier constantly tuned to the EEG signals it receives in the current session. Although the experimental results reported here show the benefits of online adaptation, some questions still need to be addressed. The chapter ends discussing some of these open issues. View full abstract»

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      Evaluation Criteria for BCI Research

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 327 - 342
      Copyright Year: 2007

      MIT Press eBook Chapters

      To analyze the performance of BCI systems, some evaluation criteria must be applied. The most popular is accuracy or error rate. Because of some strict prerequisites, accuracy is not always a suitable criterion, and other evaluation criteria have been proposed. This chapter provides an overview of evaluation criteria used in BCI research. An example from the BCI Competition 2005 is used to display results using different criteria. Within this chapter, evaluation criteria for BCI systems with more than two classes are presented, criteria for evaluating discrete and continuous output are included, and the problem of evaluating self-paced BCI operation is addressed. Special emphasis is put on discussing different methods for calculating the information transfer rate. Finally, a criterion for taking into account the response time is suggested. View full abstract»

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      BCI Software

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 343 - 345
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: Introduction View full abstract»

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      BioSig: An Open-Source Software Library for BCI Research

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 347 - 358
      Copyright Year: 2007

      MIT Press eBook Chapters

      BioSig is an open-source software library for biomedical signal processing. Besides several other application areas, BioSig also has been developed for BCI research. It provides a common interface to many different dataformats, it supports artifact processing and quality control, it provides adaptive signal processing and feature extraction methods that are very suitable for online and real-time processing, it supports handling of missing values, and it includes several classification methods for single-trial classification of EEG. This chapter provides an overview of the current status and an outline of future possibilities. BioSig is licensed with the GNU General Public License; it provides an open development platform unencumbered from legal restrictions. Therefore, it is also an ideal tool for research and development. View full abstract»

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      BCI2000: A General-Purpose Software Platform for BCI

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 359 - 368
      Copyright Year: 2007

      MIT Press eBook Chapters

      BCI2000 is a flexible general-purpose platform for brain-computer interface (BCI) research and development that is aimed mainly at reducing the complexity and cost of implementing BCI systems. Since 2000, we have been developing this system in a collaboration between the Wadsworth Center of the New York State Department of Health in Albany, New York, and the Institute of Medical Psychology and Behavioral Neurobiology at the University of Tübingen, Germany. This system currently is used for a variety of studies in more than 110 laboratories around the world. BCI2000 currently supports a variety of data acquisition systems, brain signals, and feedback modalities and can thus be configured to implement many commonly used BCI systems without any programming. We provide the source code and corresponding documentation with the system to facilitate the implementation of BCI methods that are not supported by the current system. This process, and thus the evaluation of different BCI methods, is further encouraged by the modular design of BCI2000, which is designed such that a change in a module or a component requires little or no change in other modules or components. In summary, by substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups (http://www.bci2000.org). View full abstract»

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      Applications

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 369 - 372
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: Introduction View full abstract»

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      Brain-Computer Interfaces for Communication and Motor Control—Perspectives on Clinical Applications

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 373 - 391
      Copyright Year: 2007

      MIT Press eBook Chapters

      In this overview of the state-of-the-art of clinical applications of BCIs and outlook for the future, we focus on interfaces aiming at maintaining or restoring lost communication and motor function using the electric and magnetic activity of the human cortex. View full abstract»

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      Combining BCI and Virtual Reality: Scouting Virtual Worlds

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 393 - 408
      Copyright Year: 2007

      MIT Press eBook Chapters

      A brain-computer interface (BCI) is a closed-loop system with feedback as one important component. Dependent on the BCI application either to establish communication in patients with severe motor paralysis, to control neuroprosthesis, or to perform neurofeedback, information is visually fed back to the user about success or failure of the intended act. One way to realize feedback is the use of virtual reality (VR). In this chapter, an overview is given of BCI-based control of VR. In addition, four examples are reported in more detail about navigating in virtual environments with a cue-based (synchronous) and an uncued (asynchronous) BCI. Similar results in different virtual worlds with different types of motor imageries could be achieved, but no significant differences in the BCI classification accuracy were observed between VR and non-VR feedback. Nevertheless, the use of VR stimulated the subject's task performances and provided motivation. View full abstract»

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      Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 409 - 422
      Copyright Year: 2007

      MIT Press eBook Chapters

      The ability to directly detect mental over- and under-load in human operators is an essential feature of complex monitoring and control processes. Such processes can be found, for example, in industrial production lines, in aviation, as well as in common everyday tasks such as driving. In this chapter, we present an EEG-based system that is able to detect high mental workload in drivers operating under real traffic conditions. This information is used immediately to mitigate the workload typically induced by the influx of information that is generated by the car's electronic systems. Two experimental paradigms were tested: an auditory workload scheme and a mental calculation task. The result is twofold. The system's performance is strongly subject-dependent; however, the results are good to excellent for the majority of subjects.We show that in these cases an induced mitigation of a reaction time experiment leads to an increase of the driver's overall task performance. View full abstract»

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      Single-Trial Analysis of EEG during Rapid Visual Discrimination: Enabling Cortically Coupled Computer Vision

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 423 - 439
      Copyright Year: 2007

      MIT Press eBook Chapters

      We describe our work using linear discrimination of multichannel electroencephalography for single-trial detection of neural signatures of visual recognition events. We demonstrate the approach as a methodology for relating neural variability to response variability, describing studies for response accuracy and response latency during visual target detection. We then show how the approach can be used to construct a novel type of brain-computer interface, which we term “cortically coupled computer vision.” In this application, a large database of images is triaged using the detected neural signatures. We show how “cortical triaging” improves image search over a strictly behavioral response. View full abstract»

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      References

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 441 - 490
      Copyright Year: 2007

      MIT Press eBook Chapters

      Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin. View full abstract»

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      Contributors

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 491 - 502
      Copyright Year: 2007

      MIT Press eBook Chapters

      Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin. View full abstract»

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      Index

      Dornhege, G. ; del R. Millán, J. ; Hinterberger, T. ; McFarland, D. ; Müller, K.
      Toward Brain-Computer Interfacing

      Page(s): 503 - 507
      Copyright Year: 2007

      MIT Press eBook Chapters

      Interest in developing an effective communication interface connecting the human brain and a computer has grown rapidly over the past decade. The brain-computer interface (BCI) would allow humans to operate computers, wheelchairs, prostheses, and other devices, using brain signals only. BCI research may someday provide a communication channel for patients with severe physical disabilities but intact cognitive functions, a working tool in computational neuroscience that contributes to a better understanding of the brain, and a novel independent interface for human-machine communication that offers new options for monitoring and control. This volume presents a timely overview of the latest BCI research, with contributions from many of the important research groups in the field. The book covers a broad range of topics, describing work on both noninvasive (that is, without the implantation of electrodes) and invasive approaches. Other chapters discuss relevant techniques from machine learning and signal processing, existing software for BCI, and possible applications of BCI research in the real world. Guido Dornhege is a Postdoctoral Researcher in the Intelligent Data Analysis Group at the Fraunhofer Institute for Computer Architecture and Software Technology in Berlin. Josï¿¿ï¿¿ del R. Millï¿¿ï¿¿n is a Senior Researcher at the IDIAP Research Institute in Martigny, Switzerland, and Adjunct Professor at the Swiss Federal Institute of Technology in Lausanne. Thilo Hinterberger is with the Institute of Medical Psychology at the University of Tï¿¿ï¿¿bingen and is a Senior Researcher at the University of Northampton. Dennis J. McFarland is a Research Scientist with the Laboratory of Nervous System Disorders, Wadsworth Center, New York State Department of Health. Klaus-Robert Mï¿ ¿ï¿¿ller is Head of the Intelligent Data Analysis group at the Fraunhofer Institute and Professor in the Department of Computer Science at the Technical University of Berlin. View full abstract»




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