Patrique Fiedler

Novel TiNx-based biosignal electrodes for electroencephalography

A composition, structural, morphological and electrochemical study of two titanium nitride (TiNx) thin films, sample A and sample B, obtained by dc reactive sputtering on titanium substrates was carried out in this paper. In order to assess their applicability to be used as dry electrodes, several EEG signal acquisition tests were performed using the TiNx electrodes and compared to signals acquired with conventional silver/silver-chloride (Ag/AgCl) electrodes. The two films displayed some compositional differences, as sample B was over-stoichiometric (x = 1.34, x = N/Ti atomic ratio) and sample A was close-stoichiometric (x = 0.94). XRD diffractograms showed that both samples developed a similar fcc crystalline structure (δ-TiN phase). XRD peak fitting showed that sample B (over-stoichiometric) has a more oriented structure (highly [1 1 1] textured), with larger grains. A columnar-type structure with pyramid-like shape at columns top was common to both TiNx films. This morphology, in addition to some columnar disaggregation, gives rise to a rather rough surface and porous structure. Electrochemical impedance spectroscopy results in artificial sweat showed that the electric properties of the samples remain unchanged, even after prolonged contact with sweat. The comparison of EEG signals, simultaneously recorded using the novel TiNx electrodes and conventional Ag/AgCl electrodes, showed very similar results. The root mean square deviation, which was computed for different electrode–electrolyte combinations and signal sequences of 5 s, was in the range of 4.5–9.4 µV and 6.7–20.4 µV for samples A and B, respectively. An additional test with two sets of Ag/AgCl electrodes revealed similar results, thus indicating most of the signal differences are related to the spatial distance of the compared electrodes as well as environmental noise. Therefore, our results lead to the conclusion that the novel TiNx electrodes show promising signal quality for application in EEG biosignal measurements.

Novel flexible dry PU/TiN-multipin electrodes: First application in EEG measurements

Dry biosignal electrodes for electro-encephalography (EEG) are an essential step for realization of ubiquitous EEG monitoring and brain computer interface technologies. We propose a novel electrode design with a specific shape for hair layer interfusion and reliable skin contact. An electrically conductive Titanium-Nitride (TiN) thin layer is deposited on a polyurethane substrate using a multiphase DC magnetron sputtering technique. In the current paper we describe the development and manufacturing of the electrode. Furthermore, we perform comparative EEG measurements with conventional Ag/AgCl electrodes in a 6-channel setup. Our results are promising, as the primary shape of the EEG is preserved in the signals of both electrodes sets, according to recordings of spontaneous EEG and visual evoked potentials. The variance of both signals is in the same order of magnitude. The Wilcoxon-Mann-Whitney two-sample rank-sum test revealed no significant differences for 25 of the 28 compared signal episodes. Hence, our novel electrodes show equivalent signal quality compared to conventional Ag/AgCl electrodes.

Impedance pneumography using textile electrodes

The acquisition of physiological parameters using textile and textile-integrated sensors has become an important alternative for mobile and long-term monitoring. We analyzed to different commercially available electrically conductive textiles concerning their applicability for textile-based impedance pneumography. We immersed the textiles to four corroding solutions and observed no considerable changes in the absolute value as well as the phase shift of the material impedances. Subsequently, we performed impedance pneumography tests with different current amplitudes and frequencies. Using silver coated synthetic textile electrodes it was possible to detect the correct respiration frequency during normal, flat as well as slow, deep respiration.

Novel flexible Dry multipin electrodes for EEG: Signal quality and interfacial impedance of Ti and TiN coatings

Conventional Silver/Silver-Chloride electrodes are inappropriate for routine high-density EEG and emerging new fields of application like brain computer interfaces. A novel multipin electrode design is proposed. It enables rapid and easy application while maintaining signal quality and patient comfort. The electrode design is described and impedance and EEG tests are performed with Titanium and Titanium Nitride coated electrodes. The results are compared to conventional reference electrodes in a multi-volunteer study. The calculated signal parameters prove the multipin electrode concept to reproducibly acquire EEG signal quality comparable to Ag/AgCl electrodes. The promising results encourage further investigation and can provide a technological base for future preparation-free multichannel EEG systems.

In-service characterization of a polymer wick-based quasi-dry electrode for rapid pasteless electroencephalography

Abstract A novel quasi-dry electrode prototype, based on a polymer wick structure filled with a specially designed hydrating solution is proposed for electroencephalography (EEG) applications. The new electrode does not require the use of a conventional electrolyte paste to achieve a wet, low-impedance scalp contact. When compared to standard commercial Ag/AgCl sensors, the proposed wick electrodes exhibit similar electrochemical noise and potential drift values. Lower impedances are observed when tested in human volunteers due to more effective electrode/skin contact. Furthermore, the electrodes exhibit an excellent autonomy, displaying an average interfacial impedance of 37±11 kΩ cm2 for 7 h of skin contact. After performing bipolar EEG trials in human volunteers, no substantial differences are evident in terms of shape, amplitude and spectral characteristics between signals of wick and commercial wet electrodes. Thus, the wick electrodes can be considered suitable to be used for rapid EEG applications (electrodes can be prepared without the presence of the patient) without the traditional electrolyte paste. The main advantages of these novel electrodes over the Ag/AgCl system are their low and stable impedance (obtained without conventional paste), long autonomy, comfort, lack of dirtying or damaging of the hair and because only a minimal cleaning procedure is required after the exam.

Signal Quality of Titanium and Titanium Nitride Coated Dry Polymer Electrodes

Brain Computer Interfaces, mobile monitoring and Ambient Assisted Living are new fields of application for Electroencephalography (EEG). These technologies require sensors enabling fast and easy preparation as well as mobile and long-term application. Conventional Silver/Silver-Chloride (Ag/AgCl) electrodes are inadequate due to drawbacks arising from the need for electrolyte materials, e.g. extensive skin and electrode preparation, limited application time, and multiple error sources. Novel dry electrodes are intended to be applied without additional electrolyte materials and thus provide the technological base for new EEG applications.

High frequency oscillations evoked by peripheral magnetic stimulation

The analysis of somatosensory evoked potentials (SEP) and / or fields (SEF) is a well-established and important tool for investigating the functioning of the peripheral and central human nervous system. A standard technique to evoke SEPs / SEFs is the stimulation of the median nerve by using a bipolar electrical stimulus. We aim at an alternative stimulation technique enabling stimulation of deep nerve structures while reducing patient stress and error susceptibility. In the current study, we apply a commercial transcranial magnetic stimulation system for peripheral magnetic stimulation of the median nerve. We compare the results of simultaneously recorded EEG signals to prove applicability of our technique to evoke SEPs including low frequency components (LFC) as well as high frequency oscillations (HFO). Therefore, we compare amplitude, latency and time-frequency characteristics of the SEP of 14 healthy volunteers after electric and magnetic stimulation. Both low frequency components and high frequency oscillations were detected. The HFOs were superimposed onto the primary cortical response N20. Statistical analysis revealed significantly lower amplitudes and increased latencies for LFC and HFO components after magnetic stimulation. The differences indicate the inability of magnetic stimulation to elicit supramaximal responses. A psycho-perceptual evaluation showed that magnetic stimulation was less unpleasant for 12 out of the 14 volunteers. In conclusion, we showed that LFC and HFO components related to median nerve stimulation can be evoked by peripheral magnetic stimulation.

Adaptive spatial harmonic analysis of EEG data using Laplacian eigenspace

Electroencephalography is an important diagnostic tool for functional investigations of the human brain. Recent EEG measurement technologies provide high numbers of electrodes and sampling rates, which results in a considerable quantity of data. For the analysis of this EEG data, efficient signal analysis and decomposition methods are essential. In this paper a new method for spatial harmonic analysis of EEG data using the Laplacian eigenspace of the meshed surface of electrode positions is presented. The resulting eigenspace enables the spatial harmonic analysis, filtering, denoising and decomposition of EEG data. For a proof of concept, the proposed approach is applied to an 128 channel EEG recording of visual evoked potentials. A set of harmonic spatial basis functions for the EEG electrode setup is estimated. The EEG data are spatially decomposed and low pass filtered using the harmonic spatial basis functions.

Head phantoms for electroencephalography and transcranial electric stimulation: a skull material study

Abstract Physical head phantoms allow the assessment of source reconstruction procedures in electroencephalography and electrical stimulation profiles during transcranial electric stimulation. Volume conduction in the head is strongly influenced by the skull, which represents the main conductivity barrier. Realistic modeling of its characteristics is thus important for phantom development. In the present study, we proposed plastic clay as a material for modeling the skull in phantoms. We analyzed five clay types varying in granularity and fractions of fire clay, each with firing temperatures from 550°C to 950°C. We investigated the conductivity of standardized clay samples when immersed in a 0.9% sodium chloride solution with time-resolved four-point impedance measurements. To test the reusability of the clay model, these measurements were repeated after cleaning the samples by rinsing in deionized water for 5 h. We found time-dependent impedance changes for approximately 5 min after immersion in the solution. Thereafter, the conductivities stabilized between 0.0716 S/m and 0.0224 S/m depending on clay type and firing temperatures. The reproducibility of the measurement results proved the effectiveness of the rinsing procedure. Clay provides formability, is permeable to ions, can be adjusted in conductivity value and is thus suitable for the skull modeling in phantoms.

Spatial harmonic analysis of EEG data: a comparison with PCA and ICA

s of Presentations at the International Conference on BaCI 33 FC1_3. Online Monitoring of Brain Activity C. Dinh, J. Haueisen, D. Baumgarten, and M.S. Hämäläinen Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA Biomagnetic Center, Department of Neurology, University Clinic Jena, Jena, Germany Providing millisecond temporal resolution for noninvasive mapping of human brain functions, magnetoencephalography (MEG) is optimal to monitor brain activity in real time. Realtime feedback allows the adaption of the experiment to the subject’s reaction creating a whole set of new options and increasing time efficiency by shortening acquisition and offline analysis. Whereas data analysis to date is mostly done after the acquisition process, we introduce an approach to monitor brain activity online. In order to handle the low signal-to-noise ratio (SNR) in single trials and at the same time cope with the high computational effort, the gain matrix is downsized. Since a low SNR reduces the number of distinguishable source localizations, regionwise clusters are calculated, defined by Destrieux’s brain atlas. Each cortical region is represented by a cluster dipole, that is, a standard mne-toolbox source space with 7498 dipoles is reduced to a sparse source space with 176 dipoles. The reduced number of dipoles and a preserved variance of the gain matrix improve the ability to distinguish active regions and speeds up the localization calculation at the same time. Dynamic statistical parametric mapping (dSPM) is used as localization algorithm. This algorithm is able to handle Elekta Neuromag VectorView 306-channel MEG measurements and a sampling frequency of 1000 sps online with a small delay. In case the localization is applied directly to the raw data, the minimal measurement buffer for an ordinary mobile workstation of 80 samples results in an 80-ms delay. If a larger delay is acceptable, a moving average can be applied to increase the localization accuracy. The localization output is visualized in a stereoscopic real-time brain display. First studies using both simulated and human MEG data show that the proposed real-time technique is accurate and fast. The responses to auditory and somatosensory stimuli can be 34 Clinical EEG and Neuroscience localized precisely. The stereoscopic display enables the clinician to follow the activation easily. We conclude that online brain monitoring is a useful addition to common acquisition methods and allows acquisition of more information during the measurement. This can reduce the postprocessing effort dramatically. References 1. Hämäläinen MS, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys., 1993;65:413-497. 2. Dinh C, Strohmeier D, Haueisen J, Güllmar D. Brain atlas based region of interest selection for real-time source localization using K-means lead field clustering and RAP-MUSIC. Biomed Tech (Berl). 2012;57(suppl 1). 3. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53:1-15. 4. Dale, AM, Liu AK, Fischl BR, et al. Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron. 2000;26:55-67.s of Presentations at the International Conference on BaCI 83 P077. Spatial Harmonic Analysis of EEG Data: A Comparison With PCA and ICA U. Graichen, R. Eichardt, P. Fiedler, D. Strohmeier, S. Freitag, F. Zanow, and J. Haueisen Institute of Biomedical Engineering and Informatics, Faculty of Computer Science and Automation, Ilmenau University of Technology, Ilmenau, Germany eemagine Medical Imaging Solutions GmbH, Berlin, Germany Biomagnetic Center, Department of Neurology, University Clinic Jena, Jena, Germany Introduction. In the analysis of multichannel EEG data, the spatial distribution of the measured potentials is of particular interest. We present a method for the spatial harmonic analysis (SHA) of irregularly sampled data, which can be regarded as a generalization of the Fourier analysis. Our objective is to compare SHA with the principal component analysis (PCA) and the independent component analysis (ICA) for the decomposition of EEG data. Materials and methods. The basis functions of SHA are computed by solving the Laplacian eigenvalue problem. The required Laplace–Beltrami operator was discretized by a FEM approach. The EEG data are decomposed by projection into the space of basis functions. This is similar to PCA or ICA, where projections into the space of principal or independent components are used. For the comparison, we use an own implementation of PCA and the FastICA algorithm. Somatosensory-evoked potentials were recorded in eleven healthy volunteers. The median nerve of the right forearm was stimulated by bipolar electrodes. EEG signals were recorded with 256 channels (equidistant electrode layout) and 2 coupled 128 channel amplifiers. The positions of the EEG electrodes were digitized. The data were sampled at 2048 Hz and highpass (2 Hz) and notch (50 Hz and 2 harmonics) filtered. All trials were manually checked for artifacts; the remaining trials were averaged. Results. The SEP data was decomposed by the 3 methods. The computational time to determine the SHA basis functions and the PCA components was below one second. The computational effort to determine the independent components by ICA was significantly higher (in the range of minutes). The time for data decomposition was similar for all three approaches. Three principal components (PCA), seven basis functions (SHA) and 98 independent components (ICA) out of 256 were required, on average, to describe 90% of the original signal’s energy. Conclusions. The best result in reducing the data dimensionality in our application was achieved by PCA, closely followed by SHA. The advantage of SHA is that the basis functions can be computed prior the recording of the time series, because only the sensor positions are required for its determination. This is particularly beneficial for time critical applications. Acknowledgments This work was supported by the German Federal Ministry of Economics and Technology (KF2250111ED2). Reference 1. Hyvärinen, A. IEEE Trans Neural Netw. 1999;10(3):626-634.s of Presentations at the International Conference on BaCI 61 closed by relabeling leaky spongiosa into compacta. With this repair mechanism, we achieve significantly smaller errors in the RDM* and MAG. In a third setup, we use the so-called node-shift approach, described by Wolters et al, to reshape the hexagonal elements which are involved in the leakage effect and relabeled spongiosa and CSF or skin to compacta in the respective elements. Results are further improved as demonstrated by smaller errors (RDM* and MAG) compared to the analytical solution. We conclude that the leakage effect in hexagonal FEM meshes needs to be corrected by closing the leaks via the suggested relabeling and node shifting at the relevant elements. Acknowledgments Partially funded by DFG-project KonnekFEM: WO 1425/3-1, GR 3179/3-1, HA 2899/14-1, MA 4940/1-1. References 1. Dannhauer M, Lanfer B, Wolters CH, Knosche TR. Modeling of the human skull in EEG source analysis. Hum Brain Mapp. 2011;32:1383-1399. 2. Rampersad S, Stegeman D, Oostendorp T. On handling the lay- ered structure of the skull in transcranial direct current stimu- lation models. Conf Proc IEEE Eng Med Biol Soc. 2011;2011: 1989-1992. 3. Akthari M, Bryant HC, Mamelak AN, et al. Conductivities of three-layer live human skull. Brain Topogr. 202;14:151-167. 4. Tang C, You F, Cheng G, et al. Correlation between structure and resistivity variations of the live human skull. IEEE Trans Biomed Eng. 2008;55:2286-2292. 5. Wolters CH, Kostler H, Moller C, Hardtlein J, Anwander A. Numerical approaches for dipole modeling in finite element method based source analysis. Int Congr Ser. 2007;1300:189-192. 6. Wolters CH, Anwander A, Berti G, Hartmann U. Geometry- adapted hexahedral meshes improve accuracy of finite-element- method-based EEG source analysis. IEEE Trans Biomed Eng. 2007;54:1446-1453.A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer. Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal fine- tuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems.Observability of electrical potentials from deep brain sources to surface EEG remains unclear and debated among the neuroscience community. This question is particularly crucial in the temporal lobe epilepsies investigations because they involve complex (mesial and/or lateral) epileptogenic networks (Maillard et al., 2004; Bartolomei et al, 2008). At present, when mesial structures are supposed to be epileptogenic only clinical indirect evidences are used to diagnose mesial temporal lobe (MTL) epilepsy. Based on this methodology and on drug resistance evidence, surgical treatment can be proposed without the need of invasive intracerebral investigation. Reported results of this surgery demonstrate an incomplete success (70-80%; McIntosh et al. 2012) which indicate that indirect evidences of the contribution of mesial sources are not sufficient. Seven patients undergoing pre-surgical evaluation of drug resistant epilepsy were selected from a prospective series of twenty eight patients in whom simultaneous depth and surface EEG recordings had been performed since 2009. Above these patients, three had right temporal lobe (TLE) epilepsy and four left TLE. Simultaneous SEEG-EEG signals were recorded using 128 channels placed on the same acquisition system that avoids the need to synchronize both signals. Intracerebral interictal spikes (IIS) were selected on depth EEG signals blinded to EEG signals. These IIS were triggered as temporally known (T0) brain sources due to their specific waveform and the high signal to noise ratio. Then, after IIS characterization and classification, EEG signals were automatically averaged according to the T0 markers. Averaged EEG signals were finally characterized (3D mapping, duration, amplitude and statistics) and clustered using hierarchical clustering method. Overview of the data collection and analysis process is presented in figure 1. In mean in our population, 9 depth EEG electrodes and 16 surface EEG electrodes were simultaneously used. 684±186 IIS were selected by patient for a total number of spikes in our population of 4787. According to the anatomical distribution of the IIS, 21 foci were defined and classified according to three categories: mesial (limbic structures plus collateral fissure; M, 9 foci), mesial and neocortical (M+NC, 5 foci) and neocortical part of the temporal lobe (NC, 7 foci). Comparison between SEEG spikes and averaged EEG spikes on the most activated electrode at T0 was presented in table 1. Concerning 3D Map amplitude, negative pole were always seen in the temporo-basal region for both M, M+NC and NC foci and positive pole were only observed for M+NC and NC foci. Using Walsh statistical test, 8 EEG channels in mean was presented averaged amplitude at t0 statistically different of the averaged background activity. Three different clusters were fund using the hierarchical clustering method on averaged EEG signals: 1) all patients included in the M foci class and 2) all patients included in the M+NC and NC foci class and 3) one patient with an atypical brain source. Observability of deep sources with surface EEG recordings is possible. Electrical sources from mesial temporal lobe cannot be considered as closed electrical field structures. The main problem to observe signals from these deep structures concern the signal to noise ratio. Indeed, spontaneous surface spikes originated from mesial structures cannot be seen without averaging. Hierarchical clustering method and 3D map amplitude of average EEG signals at t0 seems to indicate that M contributions was different to M+NC and NC contributions. So ICA method associated with a predetermined topography constraint should detect (without the need of simultaneous depth EEG) the mesial contribution in raw EEG signals.