Simultaneous suppression of disturbing fields and localization of magnetic markers by means of multipole expansion

BACKGROUND: Magnetically marked capsules serve for the analysis of peristalsis and throughput times within the intestinal tract. Moreover, they can be used for the targeted disposal of drugs. The capsules get localized in time by field measurements with a superconducting quantum interference device (SQUID) magnetometer array. Here it is important to ensure an online localization with high speed and high suppression of disturbing fields. In this article we use multipole expansions for the simultaneous localization and suppression of disturbing fields. METHODS: We expand the measurement data in terms of inner and outer multipoles. Thereby we obtain directly a separation of marker field and outer disturbing fields. From the inner dipoles and quadrupoles we compute the magnetization and position of the capsule. The outer multipoles get eliminated. RESULTS: The localization goodness has been analyzed depending on the order of the multipoles used and depending on the systems noise level. We found upper limits of the noise level for the usage of certain multipole moments. Given a signal to noise ratio of 40 and utilizing inner dipoles and quadrupoles and outer dipoles, the method enables an accuracy of 5 mm with a speed of 10 localizations per second. CONCLUSION: The multipole localization is an effective method and is capable of online-tracking magnetic markers

The effect of dipole housing and feeding wires in physical phantoms for EEG

Current dipoles are well established models in the localization of neuronal activity to electroencephalography (EEG) data. In physical phantoms, current dipoles can be used as signal sources. Current dipoles are often powered by constant current sources connected via twisted pair wires mostly consisting of copper. The poles are typically formed by platinum wires. These wires as well as the dipole housing might disturb the electric potential distributions in physical phantom measurements. We aimed to quantify this distortion by comparing simulation setups with and without the wires and the housing. The electric potential distributions were simulated using finite element method (FEM). We chose a homogenous volume conductor surrounding the dipoles, which was 100 times larger than the size of the dipoles. We calculated the difference of the electric potential at thesurface of the volume conductor between the simulations with and without the connecting wires and the housing. Comparing simulations neglecting all connecting wires and the housing rod to simulations considering them, the electric potential at the surface of the volume conductor differed on average by 2.85 %. Both platinum and twisted pair copper wires had a smaller effect on the electric potentials with a maximum average change of 6.38 ppm. Consequently, source localization of measurements in physical head phantoms should consider these rods in the forward model

Bad channel detection in EEG recordings

Electroencephalography (EEG) is widely used in clinical applications and basic research. Dry EEG opened the application area to new fields like self-application during gaming and neurofeedback. While recording, the signals are always affected by artefacts. Manual detection of bad channels is the gold standard in both gel-based and dry EEG but is timeconsuming. We propose a simple and robust method for automatic bad channel detection in EEG. Our method is based on the iterative calculation of standard deviations for each channel. Statistical measures of these standard deviations serve as indications for bad channel detection. We compare the new method to the results obtained from the manually identified bad channels for EEG recordings. We analysed EEG signals during resting state with eyes closed and datasets with head movement. The results showed an accuracy of 99.69 % for both gel-based and dry EEG for resting state EEG. The accuracy of our new method is 99.38 % for datasets with the head movement for both setups. There was no significant difference between the manual gold standard of bad channel identification and our iterative standard deviation method. Therefore, the proposed iterative standard deviation method can be used for bad channel detection in resting state and movement EEG recordings

A systematic comparison of deep learning methods for EEG time series analysis

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks

Using the multi-linear rank-(Lr, Lr, 1) decomposition for the detection of the 200 Hz band activity in somatosensory evoked magnetic fields and somatosensory evoked electrical potentials

Studies of oscillations in the frequency band between 80 Hz and 250 Hz for EEG (Electroencephalogram) and MEG (Magnetoencephalogram) have achieved fruitful results of detecting and interpreting both normal and pathological activities in the brain. This contribution presents a new description of the 200 Hz band activity in somatosensory evoked electrical potentials (SEPs) and somatosensory evoked magnetic fields (SEFs) with the help of tensor decompositions. The SEPs and SEFs elicited by electrical stimulation of the median nerve were measured in eight healthy volunteers. A time-frequency analysis of the SEPs and SEFs produced the time-dependent spectra of the signals that were arranged into three-dimensional EEG and MEG data tensors, respectively. We then propose a novel multi-way component analysis approach by employing a tensor decomposition known as the multi-linear rank-( $L_{r}$ , $L_{r}$ , 1) decomposition. Featuring the ability to extract channel-dependent spectral signatures, this method is able to separate the 200 Hz band activity-related signal components in SEPs and SEFs. Via a coupled version of the multi-linear rank-( $L_{r}$ , $L_{r}$ , 1) decomposition, a joint processing of these simultaneous EEG and MEG recordings has been achieved. The advantages of the joint processing over the separate processing of EEG or MEG alone have been both qualitatively and quantitatively validated in seven out of eight subjects