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ABSTRACT: OBJECTIVE: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. METHODS: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. RESULTS: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. CONCLUSION: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. SIGNIFICANCE: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).
Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 05/2013; · 3.12 Impact Factor
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ABSTRACT: There is a step of significant difficulty experienced by brain-computer interface (BCI) users when going from the calibration recording to the feedback application. This effect has been previously studied and a supervised adaptation solution has been proposed. In this paper, we suggest a simple unsupervised adaptation method of the linear discriminant analysis (LDA) classifier that effectively solves this problem by counteracting the harmful effect of nonclass-related nonstationarities in electroencephalography (EEG) during BCI sessions performed with motor imagery tasks. For this, we first introduce three types of adaptation procedures and investigate them in an offline study with 19 datasets. Then, we select one of the proposed methods and analyze it further. The chosen classifier is offline tested in data from 80 healthy users and four high spinal cord injury patients. Finally, for the first time in BCI literature, we apply this unsupervised classifier in online experiments. Additionally, we show that its performance is significantly better than the state-of-the-art supervised approach.
IEEE Transactions on Biomedical Engineering 04/2011; · 2.28 Impact Factor
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IEEE Transactions on Signal Processing. 01/2011; 59(9):4478-4482.
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ABSTRACT: We localize the sources of class-dependent event-related desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (S-FLEX). The analysis reveals focal sources in the sensorimotor cortices, a finding which can be regarded as a proof for the expected neurophysiological origin of the BCI control signal. As a technical contribution, we extend S-FLEX to the multiple measurement case in a way that the activity of different frequency bins within the mu-band is coherently localized.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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J D R Millán,
R Rupp,
G R Müller-Putz,
R Murray-Smith,
C Giugliemma,
M Tangermann,
C Vidaurre,
F Cincotti,
A Kübler,
R Leeb,
C Neuper, K-R Müller,
D Mattia
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ABSTRACT: In recent years, new research has brought the field of electroencephalogram (EEG)-based brain-computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, "Communication and Control", "Motor Substitution", "Entertainment", and "Motor Recovery". We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users' mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.
Frontiers in Neuroscience 01/2010; 4.
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Neural Networks. 06/2009;
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ABSTRACT: Designing user interfaces which can cope with unconventional control properties is challenging, and conventional interface design techniques are of little help. This paper examines how interactions can be designed to explicitly take into account the uncertainty and dynamics of control inputs. In particular, the asymmetry of feedback and control channels is highlighted as a key design constraint, which is especially obvious in current non-invasive brain–computer interfaces (BCIs). Brain–computer interfaces are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by thought. BCIs, however, have totally different signal properties than most conventional interaction devices. Bandwidth is very limited and there are comparatively long and unpredictable delays. Such interfaces cannot simply be treated as unwieldy mice. In this respect they are an example of a growing field of sensor-based interfaces which have unorthodox control properties. As a concrete example, we present the text entry application “Hex-O-Spell”, controlled via motor-imagery based electroencephalography (EEG). The system utilizes the high visual display bandwidth to help compensate for the limited control signals, where the timing of the state changes encodes most of the information. We present results showing the comparatively high performance of this interface, with entry rates exceeding seven characters per minute.
International Journal of Human-Computer Studies. 01/2009;
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Chemistry Central Journal. 01/2009;
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Chemistry Central Journal. 01/2009;
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IEEE Trans. Biomed. Eng. 01/2008;
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IEEE Signal. Proc. Mag. 01/2008; 25:41--56.
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IEEE Intell. Syst. 01/2008; 23:72--99.
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J. Neurosci. Methods. 01/2008; 167:82--90.
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ABSTRACT: This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed
on linear classification methods which can be applied in the BCI context. Finally, we provide an overview on the Berlin-Brain
Computer Interface (BBCI).
08/2007: pages 705-714;
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ABSTRACT: We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines guaranteed performance in
the learning theoretical sense and optimal search from the algorithmic point of view. Furthermore it inherits the explanatory
power of tree approaches, while improving performance over classical approaches such as CART/C4.5, as shown on experiments
on artificial and benchmark data.
Machine Learning 02/2007; 66(2):209-241. · 1.59 Impact Factor
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PLoS Comput Biol. 02/2007; 3(2):e20.
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Neuroimage. 01/2007; 37:539--550.
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Arxiv preprint arXiv:0712.2352. 01/2007;
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01/2007: pages 409Â422;
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Multimedia Tools and Applications. 01/2007; 33:73--90.
