Standardized low-resolution brain electromagnetic tomography (sLORETA) is a widely used technique for source localization. However, this technique still has some limitations, especially under realistic noisy conditions and in the case of deep sources. To overcome these problems, we present here swLORETA, an improved version of sLORETA, obtained by incorporating a singular value decomposition-based lead field weighting. We show that the precision of the source localization can further be improved by a tomographic phase synchronization analysis based on swLORETA. The phase synchronization analysis turns out to be superior to a standard linear coherence analysis, since the latter cannot distinguish between real phase locking and signal mixing.

We present a surrogate for use in nonlinear time series analysis. This surrogate algorithm has significant advantages over the most commonly used surrogates, in that it provides a more robust statistical test by producing an entire population of surrogates that are consistent with the null hypothesis. We will show that for the currently used surrogate algorithms, although individual surrogate files are consistent with the null hypothesis the population of surrogates generated is not. The surrogate is tested on a linear stochastic process and a continuous nonlinear system.

We present a noninvasive technique which allows the anatomical localization of phase synchronized neuronal populations in the human brain with magnetoencephalography. We study phase synchronization between the reconstructed current source density (CSD) of different brain areas as well as between the CSD and muscular activity. We asked four subjects to tap their fingers in synchrony with a rhythmic tone, and to continue tapping at the same rate after the tone was switched off. The phase synchronization behavior of brain areas relevant for movement coordination, inner voice, and time estimation changes drastically when the transition to internal pacing occurs, while their averaged amplitudes remain unchanged. Information of this kind cannot be derived with standard neuroimaging techniques like functional magnetic resonance imaging or positron emission tomography. [5,6] systems. In animals, phase synchronization has been demonstrated to be a fundamental mechanism for motor control [7]. Active neuronal populations in the brain generate currents which, in turn, produce a magnetic field that can noninvasively be measured by means of magnetoencephalography (MEG) with a time resolution in the millisecond range. Phase synchronization can be detected in noisy, nonstationary MEG signals [5]. However, the spatial resolution of this approach has so far been severely limited, since MEG sensors measure the magnetic field which may originate from different brain areas. To study normal brain function as well as pathological synchronization (e.g., in Parkinson's disease and epilepsy) a correct anatomical localization of synchronization processes is crucial. We here present a novel method which allows a reliable 3D localization of phase synchronization in the human brain with MEG.We first reconstructed the cerebral current source density jx; t, which generates the measured magnetic field, in each volume element (voxel) for all times t with magnetic field tomography [8]. x denotes the spatial coordinates representing the center of a voxel. We then analyzed phase synchronization voxel by voxel: To detect cerebro-muscular synchronization (CMS) we determined phase synchronization between the muscular activity recorded with electromyography (EMG) and jx; t in each of the voxels representing the brain. To detect cerebrocerebral synchronization (CCS) we determined phase synchronization of jx; t in all pairs of voxels.We applied our approach to study internal rhythm generation in humans. The latter is essential for performing rhythmic movements without external stimulus, e.g., during locomotion or skilled actions like playing muscial instruments. We performed a paced finger tapping (PFT) experiment [9], where subjects are first asked to tap with their index finger in synchrony with a periodic train of tones (external pacing). After discontinuing the tones, the subjects then have to continue the tapping at the same pace (internal pacing), by generating the rhythm alone.Behavioral studies of movement timing in PFT studies revealed an inte...

Extra-cellular neuro-recording signals used for functional mapping in deep brain stimulation (DBS) surgery and invasive brain computer interfaces, may suffer from poor signal to noise ratio. Therefore, a reliable automatic noise estimate is essential to extract spikes from recordings. We show that current methods are biased toward overestimation of noise-levels with increasing neuronal activity or artifacts. An improved and novel method is proposed that is based on an estimate of the mode of the distribution of the signal envelope. Our method makes use of the inherent characteristics of the noise distribution. For band-limited Gaussian noise the envelope of the signal is known to follow the Rayleigh distribution. The location of the peak of this distribution provides a reliable noise-level estimate. It is demonstrated that this new 'envelope' method gives superior performance both on simulated data, and on actual micro-electrode recordings made during the implantation surgery of DBS electrodes for the treatment of Parkinson's disease.

Microelectrode recording (MER) along surgical trajectories is commonly applied for refinement of the target location during deep brain stimulation (DBS) surgery. In this study, we utilize automatically detected MER features in order to locate the subthalamic nucleus (STN) employing an unsupervised algorithm. The automated algorithm makes use of background noise level, compound firing rate and power spectral density along the trajectory and applies a threshold-based method to detect the dorsal and the ventral borders of the STN. Depending on the combination of measures used for detection of the borders, the algorithm allocates confidence levels for the annotation made (i.e. high, medium and low). The algorithm has been applied to 258 trajectories obtained from 84 STN DBS implantations. MERs used in this study have not been pre-selected or pre-processed and include all the viable measurements made. Out of 258 trajectories, 239 trajectories were annotated by the surgical team as containing the STN versus 238 trajectories by the automated algorithm. The agreement level between the automatic annotations and the surgical annotations is 88%. Taking the surgical annotations as the golden standard, across all trajectories, the algorithm made true positive annotations in 231 trajectories, true negative annotations in 12 trajectories, false positive annotations in 7 trajectories and false negative annotations in 8 trajectories. We conclude that our algorithm is accurate and reliable in automatically identifying the STN and locating the dorsal and ventral borders of the nucleus, and in a near future could be implemented for on-line intra-operative use.