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

Automatic detection and prediction of discontinuities in laser beam butt welding utilizing deep learning

Laser beam butt welding of thin sheets of high-alloy steel can be really challenging due to the formation of joint gaps, affecting weld seam quality. Industrial approaches rely on massive clamping systems to limit joint gap formation. However, those systems have to be adapted for each individually component geometry, making them very cost-intensive and leading to a limited flexibility. In contrast, jigless welding can be a high flexible alternative to substitute conventionally used clamping systems. Based on the collaboration of different actuators, motions systems or robots, the approach allows an almost free workpiece positioning. As a result, jigless welding gives the possibility for influencing the formation of the joint gap by realizing an active position control. However, the realization of an active position control requires an early and reliable error prediction to counteract the formation of joint gaps during laser beam welding. This paper proposes different approaches to predict the formation of joint gaps and gap induced weld discontinuities in terms of lack of fusion based on optical and tactile sensor data. Our approach achieves 97.4 % accuracy for video-based weld discontinuity detection and a mean absolute error of 0.02 mm to predict the formation of joint gaps based on tactile length measurements by using inductive probes

Magnetic Resonance Imaging for the in Vivo Evaluation of Gastric-Retentive Tablets

Purpose. To develop a magnetic resonance imaging (MRI) technique for assessing in vivo properties of orally ingested gastric-retentive tablets under physiologic conditions. Methods. Tablets with different floating characteristics (tablet A-C) were marked with superparamagnetic Fe3O4 particles to analyze intragastric tablet position and residence time in human volunteers. Optimal Fe3O4 concentration was determined in vitro. Intragastric release characteristic of one slow-release tablet (tablet D) was analyzed by embedding gadolinium chelates (Gd-DOTA) as a drug model into the tablet. All volunteers underwent MRI in the sitting position. Tablet performance was analyzed in terms of relative position of tablet to intragastric meal level (with 100% at meal surface), intragastric residence time (min) and Gd-DOTA distribution volume (% of meal volume). Results. Intragastric tablet floating performance and residence time of tablets (tablet A-D) as well as the intragastric Gd-DOTA distribution of tablet D could be monitored using MRI. Tablet floating performance was different between the tablets (A, 93%(95 − 9%); B, 80%(80 − 68%); C, 38%(63 − 32%); p < 0.05). The intragastric distribution volume of Gd-DOTA was 19.9% proximally and 35.5% distally. Conclusions. The use of MRI allows the assessment of galenic properties of orally ingested tablets in humans in seated positio

R&D Status of the New Superconducting CW Heavy Ion LINAC@GSI

To keep the ambitious Super Heavy Element (SHE) physics program at GSI competitive a superconducting (sc) continuous wave (cw) high intensity heavy ion LINAC is currently under progress as a multi-stage R&D program of GSI, HIM and IAP*. The baseline linac design consists of a high performance ion source, a new low energy beam transport line, an (cw) upgraded High Charge State Injector (HLI), and a matching line (1.4 MeV/u) which is followed by the new sc-DTL LINAC for post acceleration up to 7.3 MeV/u. In the present design the new cw-heavy ion LINAC comprises constant-beta sc Crossbar-H-mode (CH) cavities operated at 217 MHz. The advantages of the proposed beam dynamics concept applying a constant beta profile are easy manufacturing with minimized costs as well as a straightforward energy variation**. An important milestone will be the full performance test of the first CH cavity (Demonstrator), in a horizontal cryo module with beam. An advanced demonstrator setup comprising a string of cavities and focussing elements is proposed to build from 10 short CH-cavities with 8 gaps. The corresponding simulations and technical layout of the new cw heavy ion LINAC will be presented

Magnetic resonance imaging for the in vivo evaluation of gastric-retentive tablets

The use of MRI allows the assessment of galenic properties of orally ingested tablets in humans in seated position