Power Line Noise Removal Research Articles

Mobile brain imaging in butoh dancers: from rehearsals to public performance.

Dissecting the neurobiology of dance would shed light on a complex, yet ubiquitous, form of human communication. In this experiment, we sought to study, via mobile electroencephalography (EEG), the brain activity of five experienced dancers while dancing butoh, a postmodern dance that originated in Japan. We report the experimental design, methods, and practical execution of a highly interdisciplinary project that required the collaboration of dancers, engineers, neuroscientists, musicians, and multimedia artists, among others. We explain in detail how we technically validated all our EEG procedures (e.g., via impedance value monitoring) and minimized potential artifacts in our recordings (e.g., via electrooculography and inertial measurement units). We also describe the engineering details and hardware that enabled us to achieve synchronization between signals recorded at different sampling frequencies, along with a signal preprocessing and denoising pipeline that we used for data re-sampling and power line noise removal. As our experiment culminated in a live performance, where we generated a real-time visualization of the dancers' interbrain synchrony on a screen via an artistic brain-computer interface, we outline all the methodology (e.g., filtering, time-windows, equation) we used for online bispectrum estimations. Additionally, we provide access to all the raw EEG data and codes we used in our recordings. We, lastly, discuss how we envision that the data could be used to address several hypotheses, such as that of interbrain synchrony or the motor theory of vocal learning. Being, to our knowledge, the first study to report synchronous and simultaneous recording from five dancers, we expect that our findings will inform future art-science collaborations, as well as dance-movement therapies.

Fast Removal of Powerline Harmonic Noise From Surface NMR Datasets Using a Projection-Based Approach on Graphical Processing Units

Surface nuclear magnetic resonance (NMR) measurements are notorious for their low signal-to-noise ratio (SNR). Powerlines are probably the most common source of noise and give the greatest contribution to noise levels. The noise from powerlines manifests itself as sinusoidal signals oscillating at the fundamental powerline frequency (50 or 60 Hz) and at integer multiples of this frequency. Modeling and subtraction of the powerline noise have been demonstrated as a highly applicable method for improving SNR and are common practice today. However, the methods used to determine the parameters of the powerline noise are computationally expensive. Consequently, it is difficult to do real-time noise removal during the acquisition of field data and, therefore, also difficult to do a real-time quality inspection of data. Here, we demonstrate how the removal of powerline noise in surface NMR data can be significantly faster. We obtain this through two new developments. First, we apply a projection-based method to determine the powerline model, which is twice as fast as the commonly applied least-squares solution of a matrix equation. Second, we obtain a further 10–25 times speed-up by exploiting the high-performance parallel computations offered by graphical processing units (GPUs). We demonstrate the method on a noise-only field dataset with an embedded synthetic NMR signal.

Removal of Powerline Noise in Geophysical Datasets With a Scientific Machine-Learning Based Approach

The most common noise in geophysical data is probably the interference from powerlines. This noise manifests itself as a sinusoidal signal oscillating at the fundamental 50 Hz or 60 Hz frequency of the power grid and as harmonic components oscillating at integer multiples. Many different mitigation strategies, tailored for the specific geophysical method, have been developed to target powerline noise. One method that applies to fully sampled data is model-based subtraction, where a model of the powerline noise is fitted to the noisy data set and subsequently subtracted. In most cases, this leads to significant improvements in the signal-to-noise ratio. However, the determination of the powerline model parameters, in particular the fundamental powerline frequency, is computationally expensive, as it requires repeated solutions of a least-squares problem. We demonstrate that the powerline frequency can be directly predicted with a scientific machine-learning based approach. We work on both time domain induced polarization and surface nuclear magnetic resonance data. We use a different network for each method to trade-off prediction accuracy and prediction speed. In both cases, the prediction accuracy is fully on par with standard methods, and we obtain speed-ups by factors of 400 and 10 for the two types of data.

Surface Laplacian based Single Channel Activity (SCA) in control subjects and stroke patients

Event Abstract Back to Event Surface Laplacian based Single Channel Activity (SCA) in control subjects and stroke patients K. Fulop1*, Vegso B1, Cs Szekrenyesi1, T. Magos1, M. Dombovari1, Gy Kozmann1 and Z. Nagy1 1 Universtity of Pannonia, Department of Electrical Engineering and Information Systems, Faculty of Information Technology, Hungary Aim: Sequential activity of cortical areas is responsible for preparation and execution of motor activity. High power 128 channels EEG with its 0.5 msec temporal resolution promises an appropriate technique to study brain function in a "mesoscopic" level. Methods: Visually guided left- and right-hand finger tapping experiments are carried out in an electrically shielded room on right handed subjects using a 128+9 channels EEG device at 2048Hz sampling rate. The tapping is performed at a predefined pace with 10 seconds inter-tap interval, 32 tapping epochs are selected for processing. Power line noise removal is followed by band-passing the signal to 1-400Hz, leaving the selected 3072 sample (1500ms) data epoch clean. A 500ms pre-tapping interval is selected as baseline phase, 500-500ms before and after tapping intervals are considered for further processing. The 128 scalp electrodes are grouped by anatomical relevance to the task. Two dimensional discrete Surface Laplacian calculations allow for estimation of radial cortical surface current density (SCD), which is averaged over the 32 selected tapping epochs. Threshold values are calculated from the baseline interval to determine radial current density limits of activation (Single Channel Activity - SCA). The activation level of the anatomically defined regions were characterised by the accumulated SCD values. Results: Age related increase of activated SCA was documented in control subjects. In young age group the relevant motor and premotor activity was moderate compared to the elderly control subjects. Immediately following stroke the relevant activity was poorly detectable while in the course of rehabilitation the typical motor and premotor activity has returned. Conclusion: We developed a new Surface Laplacian based technology (SCA) [1] capable of characterizing task related activation of motor and premotor cortex and following the post-stroke repair phenomenon. The technique also proved to be a useful tool for assessing lateralization. Acknowledgments: Granted by NeuroMath Action BM0601, OTKA K69240, NTP OM00191/2008 AALAMSRK.