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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Fodil Zerrouki; Salah Haddab;

    The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the n repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the n repetitions.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Clinical EEG and Neu...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Clinical EEG and Neuroscience
    Article . 2022 . Peer-reviewed
    License: SAGE TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Clinical EEG and Neu...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Clinical EEG and Neuroscience
      Article . 2022 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zheng Wu; Jiahua Xu; Andreas Nürnberger; Bernhard A Sabel;

    AbstractTightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities (“modules”) during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients’ recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cerebral Cortexarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Cerebral Cortex
    Article . 2022 . Peer-reviewed
    License: OUP Standard Publication Reuse
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    Cerebral Cortex
    Article . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cerebral Cortexarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Cerebral Cortex
      Article . 2022 . Peer-reviewed
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      Cerebral Cortex
      Article . 2022
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Christa Neuper; Michael Wörtz; Gert Pfurtscheller;

    Oscillations in the alpha and beta band (<35 Hz) show characteristic spatiotemporal patterns during sensorimotor processing. Whereas event-related desynchronization (ERD) during motor preparation, execution, and imagery can be seen as a correlate of an activated cortical area, event-related synchronization (ERS) of frequency components between 10 and 13 Hz may represent a deactivated cortical area or inhibited cortical network, at least under certain conditions. Induced beta rhythms (13–35 Hz, beta ERS) can be found in sensorimotor areas following both voluntary movement and somatosensory stimulation. In a recent study we used different tasks involving execution and imagery of movements of the upper and lower limb to produce activation vs. deactivation/inhibition of the sensorimotor hand area. Sensorimotor interference, as a function of the activation level of the motor cortex, was studied by the use of repetitive median nerve stimulation (MNS) (ISI 1.5 s) in 12 healthy volunteers during the following task conditions: (i) cube manipulation between thumb and fingers of one hand, (ii) imagined cube manipulation, (iii) continuous foot rotation movements, and (iv) imagined foot movements. EEG was recorded from hand and foot representation areas and processed time-locked to MNS (ERD/ERS). In addition, task-related band power changes (TRPD/TRPI) were analyzed. We found a clear-cut suppression of the stimulation-induced beta ERS (indicating an enhanced activity state of the sensorimotor areas) during active cube manipulation and a weaker suppression during cube imagery. Mental imagination of foot movement led to an increase of the hand area mu rhythm, but did not interfere with stimulation-related effects on beta ERS. These findings support that interfering sensorimotor activation and deactivation is reflected in graduated changes of induced mu and beta oscillations.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1016/s0079-...
    Part of book or chapter of book . 2006 . Peer-reviewed
    License: Elsevier TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.1016/s0079-...
      Part of book or chapter of book . 2006 . Peer-reviewed
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Thomas, Neumann; Anne Katrin, Baum; Ulrike, Baum; Renate, Deike; +4 Authors

    Abstract Objectives The HOME project is intended to provide evidence of diagnostic and therapeutic yield of a patient-controlled EEG home-monitoring for neurological outpatients. Methods This study evaluated the technical and practical usability and efficacy of a new portable dry-electrode EEG recorder in comparison to conventional EEG devices based on technical assessments and inter-rater comparisons of EEG record examinations of office-based practitioners and two experienced neurologists. Results The technical assessment was based on channel-wise comparisons of band power values derived from power spectra as observed in two recording modalities. Slight yet significant differences were observed only in the Delta-frequency band (1.5–4 Hz). The fraction of automatically detected artifact segments was larger in the new portable recordings than in conventional recordings (20% vs. 11%, median). Overall, 93% of raters’ stated diagnostic findings gathered from conventional devices were concordant with stated diagnostic findings gathered from the new portable device. Conclusion The new EEG device was shown to have technical comparability to and a high concordance rate of diagnostic findings with conventional EEG devices. Significance The new portable dry-electrode EEG device is suitable to meet the HOME projects’ goal of establishing a patient-controlled EEG home-monitoring in the routine care of neurological outpatients. Trial registration DRKS DRKS00012685. Registered 09 August 2017, retrospectively registered.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DZNE Pubarrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    DZNE Pub
    Article . 2019
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Clinical Neurophysiology
    Article . 2019 . Peer-reviewed
    License: Elsevier TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao DZNE Pubarrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      DZNE Pub
      Article . 2019
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Clinical Neurophysiology
      Article . 2019 . Peer-reviewed
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  • Authors: Malika-Djahida Kedir-Talha; Mohamed Amine Ait Amer;

    The compression of Electroencephalographic (EEG) signal is of great interest to many in the biomedical community. The motivation for this is the large amount of data involved in collecting EEG information which requires more memory for storage and high bandwidth for transmission. This work shows the contribution of biomedical signal processing by lifted wavelet transform LWT, in the field of encephalic signal compression. Our results show a high performance compression for normal and pathological EEG. A statistical study on a set of 200 signals healthy and epileptic, confirms these results. The choice of wavelet rbior 5.5 is best suited to the shape of the EEG signal, it allows to increasing the compression ratio and to ensuring a safe recovery. The increase, in the level decomposition and the threshold, allows to increasing the compression ratio while keeping a good performance of reconstitution. Our study allows us to choose the LWT rbio5.5 as tool for encephalic signal compression with a compression ratio of 83.16% and a recovery rate of 99.95%.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Christa Neuper; Gernot Müller-Putz; Reinhold Scherer; Gert Pfurtscheller;

    A brain-computer interface (BCI) transforms signals originating from the human brain into commands that can control devices or applications. With this, a BCI provides a new non-muscular communication channel, which can be used to assist patients who have highly compromised motor functions. The Graz-BCI uses motor imagery and associated oscillatory EEG signals from the sensorimotor cortex for device control. As a result of research in the past 15 years, the classification of ERD/ERS patterns in single EEG trials during motor execution and motor imagery forms the basis of this sensorimotor-rhythm controlled BCI. The major frequency bands of cortical oscillations considered here are the 8-13 and 15-30 Hz bands. This chapter describes the basic methods used in Graz-BCI research and outlines possible clinical applications.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1016/s0079-...
    Part of book or chapter of book . 2006 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao https://doi.org/10.1...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.1016/s0079-...
      Part of book or chapter of book . 2006 . Peer-reviewed
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  • Authors: Yongjie, Zhu; Xiaoyu, Wang; Klaus, Mathiak; Petri, Toiviainen; +4 Authors
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  • Authors: Mahmoud Boudiaf; Moncef Benkherrat; Khaled Mansouri;

    This study presents a method for improving the signal-to-noise ratio of single-trial event-related potentials. The method is based on adaptive linear combiner Hermite model. A variable step size least mean square algorithm is used to estimate and to adjust the parameters of the filter. The performances of the method are applied to simulated data and real event-related potential recordings. The method significantly enhances the observation of single trials and the estimation of amplitude and latency of the event-related potentials.

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  • Authors: Manel Yakoubi; Rachid Hamdi; Mounir Bousbia Salah;

    The Electroencephalogram(EEG) is a representative signal enclosing information about the function of the brain. EEG signals can be utilized to study an automatic diagnosis of epilepsy. This later is characterized by a spontaneous and an unexpected that occurs seizures. In this paper, we propose an automatic detection of seizure disease using a support vector machine (SVM) classifier for the classification of EEG signals from three classes like healthy, pre-ictal and epileptic patients. Five statistical time domain features are extracted namely Root Mean Square (RMS), Sekwens (Sk), kurtosis (Ku), crest factor (CF) and clearance indicator (CLI). The Generalized Discriminant Analysis (GDA) is applied on statistical time domain features to reduce the dimensionality. These features with reduced dimensionality are utilized as input to a support vector machine (SVM) classifier. Our results in terms of classification accuracy out performs other existing methods and achieve the best classification rates with an average of 98.80%. Our approach is effective to diagnosis and detect epileptic seizure.

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  • Authors: Mohamed Amine Hadj-Youcef; Mourad Adnane; A. Bousbia-Salah;

    In this paper the problematic of epileptic detection is treated. An algorithm of EEG signal classification into two classes: Healthy and Epileptics is developed. The difference with conventional methods is the use of free seizure epileptic records. A good classification accuracy means that it is possible to detect an epileptic in normal state or at an early stage of epilepsy. The raw EEG signal is decomposed using discrete wavelet transform (DWT). Then, principal component analysis (PCA) allows dimensionality reduction and better representation of the data. Several features are extracted and used in support vector machine (SVM) classifier. Results show satisfactory classification accuracy comparable or better than those reported in literature.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Fodil Zerrouki; Salah Haddab;

    The P300 speller Machine is among the leading applications of the electroencephalography (EEG)-based brain computer interfaces (BCIs), it is still a benchmark and a persistent challenge for the BCI Community. EEG signal classification represents the key piece of a BCI chain. The minimum distance to Riemannian mean (MDRM) belongs to these classification methods emerging in different BCI applications such as text spelling by thought. Based on a binary classification of each covariance matrix separately, character prediction is done according to the highest score across the whole set of all repetitions. Minimum cumulative distance to Riemannian mean (MCDRM) is a Cumulative variant of the MDRM, perfectly adapted to the P300 Speller Machine. The power of this variant is that prediction takes a more global proceeding involving the n repetitions together. Indeed, thanks to cumulative distances selected row and column are those having the covariance matrices both closer to the Target barycenter and farther from the non-Target one. This variant overcomes the main MDRM limitations as it improves inter-sessional generalization, allows optimal use of all repetitions and reduces considerably the risk of conflict appearing during the selection of rows and columns leading to character prediction. We applied this variant to the raw signals of Data set II-b of Berlin BCI and compared to the published results the MCDRM offers significantly higher results: 97.5% of correct predictions compared to the 96.5% of the competition winner. The MCDRM fits best with the P300 Speller machine, especially when dealing with noisy signals that requires intelligent and optimal usage of the n repetitions.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Clinical EEG and Neu...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Clinical EEG and Neuroscience
    Article . 2022 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Clinical EEG and Neu...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Clinical EEG and Neuroscience
      Article . 2022 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zheng Wu; Jiahua Xu; Andreas Nürnberger; Bernhard A Sabel;

    AbstractTightly connected clusters of nodes, called communities, interact in a time-dependent manner in brain functional connectivity networks (FCN) to support complex cognitive functions. However, little is known if and how different nodes synchronize their neural interactions to form functional communities (“modules”) during visual processing and if and how this modularity changes postlesion (progression or recovery) following neuromodulation. Using the damaged optic nerve as a paradigm, we now studied brain FCN modularity dynamics to better understand module interactions and dynamic reconfigurations before and after neuromodulation with noninvasive repetitive transorbital alternating current stimulation (rtACS). We found that in both patients and controls, local intermodule interactions correlated with visual performance. However, patients’ recovery of vision after treatment with rtACS was associated with improved interaction strength of pathways linked to the attention module, and it improved global modularity and increased the stability of FCN. Our results show that temporal coordination of multiple cortical modules and intermodule interaction are functionally relevant for visual processing. This modularity can be neuromodulated with tACS, which induces a more optimal balanced and stable multilayer modular structure for visual processing by enhancing the interaction of neural pathways with the attention network module.

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    Cerebral Cortex
    Article . 2022 . Peer-reviewed
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      Cerebral Cortex
      Article . 2022 . Peer-reviewed
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      Article . 2022
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Christa Neuper; Michael Wörtz; Gert Pfurtscheller;

    Oscillations in the alpha and beta band (<35 Hz) show characteristic spatiotemporal patterns during sensorimotor processing. Whereas event-related desynchronization (ERD) during motor preparation, execution, and imagery can be seen as a correlate of an activated cortical area, event-related synchronization (ERS) of frequency components between 10 and 13 Hz may represent a deactivated cortical area or inhibited cortical network, at least under certain conditions. Induced beta rhythms (13–35 Hz, beta ERS) can be found in sensorimotor areas following both voluntary movement and somatosensory stimulation. In a recent study we used different tasks involving execution and imagery of movements of the upper and lower limb to produce activation vs. deactivation/inhibition of the sensorimotor hand area. Sensorimotor interference, as a function of the activation level of the motor cortex, was studied by the use of repetitive median nerve stimulation (MNS) (ISI 1.5 s) in 12 healthy volunteers during the following task conditions: (i) cube manipulation between thumb and fingers of one hand, (ii) imagined cube manipulation, (iii) continuous foot rotation movements, and (iv) imagined foot movements. EEG was recorded from hand and foot representation areas and processed time-locked to MNS (ERD/ERS). In addition, task-related band power changes (TRPD/TRPI) were analyzed. We found a clear-cut suppression of the stimulation-induced beta ERS (indicating an enhanced activity state of the sensorimotor areas) during active cube manipulation and a weaker suppression during cube imagery. Mental imagination of foot movement led to an increase of the hand area mu rhythm, but did not interfere with stimulation-related effects on beta ERS. These findings support that interfering sensorimotor activation and deactivation is reflected in graduated changes of induced mu and beta oscillations.

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    https://doi.org/10.1016/s0079-...
    Part of book or chapter of book . 2006 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.1016/s0079-...
      Part of book or chapter of book . 2006 . Peer-reviewed
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Thomas, Neumann; Anne Katrin, Baum; Ulrike, Baum; Renate, Deike; +4 Authors

    Abstract Objectives The HOME project is intended to provide evidence of diagnostic and therapeutic yield of a patient-controlled EEG home-monitoring for neurological outpatients. Methods This study evaluated the technical and practical usability and efficacy of a new portable dry-electrode EEG recorder in comparison to conventional EEG devices based on technical assessments and inter-rater comparisons of EEG record examinations of office-based practitioners and two experienced neurologists. Results The technical assessment was based on channel-wise comparisons of band power values derived from power spectra as observed in two recording modalities. Slight yet significant differences were observed only in the Delta-frequency band (1.5–4 Hz). The fraction of automatically detected artifact segments was larger in the new portable recordings than in conventional recordings (20% vs. 11%, median). Overall, 93% of raters’ stated diagnostic findings gathered from conventional devices were concordant with stated diagnostic findings gathered from the new portable device. Conclusion The new EEG device was shown to have technical comparability to and a high concordance rate of diagnostic findings with conventional EEG devices. Significance The new portable dry-electrode EEG device is suitable to meet the HOME projects’ goal of establishing a patient-controlled EEG home-monitoring in the routine care of neurological outpatients. Trial registration DRKS DRKS00012685. Registered 09 August 2017, retrospectively registered.

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    DZNE Pub
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    Clinical Neurophysiology
    Article . 2019 . Peer-reviewed
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      Clinical Neurophysiology
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  • Authors: Malika-Djahida Kedir-Talha; Mohamed Amine Ait Amer;

    The compression of Electroencephalographic (EEG) signal is of great interest to many in the biomedical community. The motivation for this is the large amount of data involved in collecting EEG information which requires more memory for storage and high bandwidth for transmission. This work shows the contribution of biomedical signal processing by lifted wavelet transform LWT, in the field of encephalic signal compression. Our results show a high performance compression for normal and pathological EEG. A statistical study on a set of 200 signals healthy and epileptic, confirms these results. The choice of wavelet rbior 5.5 is best suited to the shape of the EEG signal, it allows to increasing the compression ratio and to ensuring a safe recovery. The increase, in the level decomposition and the threshold, allows to increasing the compression ratio while keeping a good performance of reconstitution. Our study allows us to choose the LWT rbio5.5 as tool for encephalic signal compression with a compression ratio of 83.16% and a recovery rate of 99.95%.

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    Authors: Christa Neuper; Gernot Müller-Putz; Reinhold Scherer; Gert Pfurtscheller;

    A brain-computer interface (BCI) transforms signals originating from the human brain into commands that can control devices or applications. With this, a BCI provides a new non-muscular communication channel, which can be used to assist patients who have highly compromised motor functions. The Graz-BCI uses motor imagery and associated oscillatory EEG signals from the sensorimotor cortex for device control. As a result of research in the past 15 years, the classification of ERD/ERS patterns in single EEG trials during motor execution and motor imagery forms the basis of this sensorimotor-rhythm controlled BCI. The major frequency bands of cortical oscillations considered here are the 8-13 and 15-30 Hz bands. This chapter describes the basic methods used in Graz-BCI research and outlines possible clinical applications.

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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    https://doi.org/10.1016/s0079-...
    Part of book or chapter of book . 2006 . Peer-reviewed
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      https://doi.org/10.1016/s0079-...
      Part of book or chapter of book . 2006 . Peer-reviewed
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  • Authors: Yongjie, Zhu; Xiaoyu, Wang; Klaus, Mathiak; Petri, Toiviainen; +4 Authors
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  • Authors: Mahmoud Boudiaf; Moncef Benkherrat; Khaled Mansouri;

    This study presents a method for improving the signal-to-noise ratio of single-trial event-related potentials. The method is based on adaptive linear combiner Hermite model. A variable step size least mean square algorithm is used to estimate and to adjust the parameters of the filter. The performances of the method are applied to simulated data and real event-related potential recordings. The method significantly enhances the observation of single trials and the estimation of amplitude and latency of the event-related potentials.

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  • Authors: Manel Yakoubi; Rachid Hamdi; Mounir Bousbia Salah;

    The Electroencephalogram(EEG) is a representative signal enclosing information about the function of the brain. EEG signals can be utilized to study an automatic diagnosis of epilepsy. This later is characterized by a spontaneous and an unexpected that occurs seizures. In this paper, we propose an automatic detection of seizure disease using a support vector machine (SVM) classifier for the classification of EEG signals from three classes like healthy, pre-ictal and epileptic patients. Five statistical time domain features are extracted namely Root Mean Square (RMS), Sekwens (Sk), kurtosis (Ku), crest factor (CF) and clearance indicator (CLI). The Generalized Discriminant Analysis (GDA) is applied on statistical time domain features to reduce the dimensionality. These features with reduced dimensionality are utilized as input to a support vector machine (SVM) classifier. Our results in terms of classification accuracy out performs other existing methods and achieve the best classification rates with an average of 98.80%. Our approach is effective to diagnosis and detect epileptic seizure.

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  • Authors: Mohamed Amine Hadj-Youcef; Mourad Adnane; A. Bousbia-Salah;

    In this paper the problematic of epileptic detection is treated. An algorithm of EEG signal classification into two classes: Healthy and Epileptics is developed. The difference with conventional methods is the use of free seizure epileptic records. A good classification accuracy means that it is possible to detect an epileptic in normal state or at an early stage of epilepsy. The raw EEG signal is decomposed using discrete wavelet transform (DWT). Then, principal component analysis (PCA) allows dimensionality reduction and better representation of the data. Several features are extracted and used in support vector machine (SVM) classifier. Results show satisfactory classification accuracy comparable or better than those reported in literature.

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