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Florian Losch

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3 papers

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YNIMG Journal 2011 Journal Article

Optimal imaging of cortico-muscular coherence through a novel regression technique based on multi-channel EEG and un-rectified EMG

Zubeyir Bayraktaroglu
Katherina von Carlowitz-Ghori
Florian Losch
Guido Nolte
Gabriel Curio
Vadim V. Nikulin

Cortico-muscular coherence (CMC) reflects interactions between muscular and cortical activities as detected with EMG and EEG recordings, respectively. Most previous studies utilized EMG rectification for CMC calculation. Yet, recent modeling studies predicted that EMG rectification might have disadvantages for CMC evaluation. In addition, previously the effect of rectification on CMC was estimated with single-channel EEG which might be suboptimal for detection of CMC. In order to optimally detect CMC with un-rectified EMG and resolve the issue of EMG rectification for CMC estimation, we introduce a novel method, Regression CMC (R-CMC), which maximizes the coherence between EEG and EMG. The core idea is to use multiple regression where narrowly filtered EEG signals serve as predictors and EMG is the dependent variable. We investigated CMC during isometric contraction of the abductor pollicis brevis muscle. In order to facilitate the comparison with previous studies, we estimated the effect of rectification with frequently used Laplacian filtering and C3/C4 vs. linked earlobes. For all three types of analysis, we detected CMC in the beta frequency range above the contralateral sensorimotor areas. The R-CMC approach was validated with simulations and real data and was found capable of recovering CMC even in case of high levels of background noise. When using single channel data, there were no changes in the strength of CMC estimated with rectified or un-rectified EMG — in agreement with the previous findings. Critically, for both Laplacian and R-CMC analyses EMG rectification resulted in significantly smaller CMC values compared to un-rectified EMG. Thus, the present results provide empirical evidence for the predictions from the earlier modeling studies that rectification of EMG can reduce CMC.

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YNIMG Journal 2005 Journal Article

Electroencephalographic spectral power in writer's cramp patients: Evidence for motor cortex malfunctioning during the cramp

Rumyana Kristeva
Vihren Chakarov
Florian Losch
Sibylla Hummel
Traian Popa
Jürgen Schulte-Mönting

We investigated cortical activation as reflected in task-related spectral power (TRPow) changes in 8 writer's cramp patients during writing on a digital board and during isometric contraction and compared them to those of 8 age-matched healthy subjects. Scalp EEG was recorded over the contralateral primary sensorimotor area (SM1c), and from the ipsilateral sensorimotor area (SM1i). The electromyogram (EMG) was recorded from the Extensor Digitorum Communis (Extensor), Flexor Digitorum Superficialis (Flexor), and First Dorsal Interosseous (FDI) muscles. We analyzed (1) handwriting performance, (2) changes in the TRPow confined to alpha and beta band, and (3) the EMG spectral power during both tasks, writing and isometric contraction. During writing, all patients developed writer's cramp. The handwriting in writer's cramp patients was associated with significantly less reduction of the beta-range TRPow and lower frequency of the TRPow reduction compared to controls. No significant differences between patients and controls for the alpha band TRPow reduction during handwriting were observed. During writing, the patients showed higher EMG spectral power than the controls but this difference was at the border of significance. The present results indicate disorder in the motor execution system, in writer's cramp patients, associated with impaired functional beta-network state of the contra- and ipsilateral sensorimotor cortices, most probably due to inadequate modulation of the intracortical inhibition associated with writing.

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NeurIPS Conference 2005 Conference Paper

Optimizing spatio-temporal filters for improving Brain-Computer Interfacing

Guido Dornhege
Benjamin Blankertz
Matthias Krauledat
Florian Losch
Gabriel Curio
Klaus-Robert Müller

Brain-Computer Interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the clas- siﬁcation of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral ﬁlter enhancing discriminability of multi-channel EEG single-trials. The eval- uation of 60 experiments involving 22 different subjects demonstrates the superiority of the proposed algorithm. Apart from the enhanced clas- siﬁcation, the spatial and/or the spectral ﬁlter that are determined by the algorithm can also be used for further analysis of the data, e. g. , for source localization of the respective brain rhythms.

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