The role of distinct ECoG frequency features in decoding finger movement.

Objective.To identify the electrocorticography (ECoG) frequency features that encode distinct finger movement states during repeated finger flexions.Approach.We used the publicly available Stanford ECoG dataset of cue-based, repeated single finger flexions. Using linear regression, we identified the spectral features that contributed most to the encoding of movement dynamics and discriminating movement events from rest, and combined them to predict finger movement trajectories. Furthermore, we also looked into the effect of the used frequency range and the spatial distribution of the identified features.Main results.Two frequency features generate superior performance, each one for a different movement aspect: high gamma band activity distinguishes movement events from rest, whereas the local motor potential (LMP) codes for movement dynamics. Combining these two features in a finger movement decoder outperformed comparable prior work where the entire spectrum was used as the average correlation coefficient with the true trajectories increased from 0.45 to 0.5, both applied to the Stanford dataset, and erroneous predictions during rest were demoted. In addition, for the first time, our results show the influence of the upper cut-off frequency used to extract LMP, yielding a higher performance when this range is adjusted to the finger movement rate.Significance.This study shows the benefit of a detailed feature analysis prior to designing the finger movement decoder.

Finger movement and coactivation predicted from intracranial brain activity using extended block-term tensor regression.

Objective.We introduce extended Block-Term Tensor Regression (eBTTR), a novel regression method designed to account for the multilinear nature of human intracranial finger movement recordings.Approach.The proposed method relies on recursive Tucker decomposition combined with automatic component extraction.Main results.eBTTR outperforms state-of-the-art regression approaches, including multilinear and deep learning ones, in accurately predicting finger trajectories as well as unintentional finger coactivations.Significance.eBTTR rivals state-of-the-art approaches while being less computationally expensive which is an advantage when intracranial electrodes are implanted acutely, as part of the patient's presurgical workup, limiting time for decoder development and testing.

Source space reduction for eLORETA.

Objective.We introduce Sparse exact low resolution electromagnetic tomography (eLORETA), a novel method for estimating a nonparametric solution to the source localization problem. Its goal is to generate a sparser solution compared to other source localization methods including eLORETA while benefitting from the latter's superior source localization accuracy.Approach.Sparse eLORETA starts by reducing the source space of the Lead Field Matrix using structured sparse Bayesian learning from which a Reduced Lead Field Matrix is constructed, which is used as input to eLORETA.Main results.With Sparse eLORETA, source sparsity can be traded against signal fidelity; the proposed optimum is shown to yield a much sparser solution than eLORETA's with only a slight loss in signal fidelity.Significance.When pursuing a data-driven approach, for cases where it is difficult to choose specific regions of interest, or when subsequently a connectivity analysis is performed, source space reduction could prove beneficial.

A comparative performance analysis of stand-alone, off-grid solar-powered sodium hypochlorite generators.

Sodium hypochlorite (NaClO) is a chemical commodity widely employed as a disinfection agent in water treatment applications. Its production commonly follows electrochemical routes in an undivided reactor. Powering the process with photovoltaic (PV) electricity holds the potential to install stand-alone, independent generators and reduce the NaClO production cost. This study reports the comparative assessment of autonomous, solar-powered sodium hypochlorite generators employing different photovoltaic (PV) technologies: silicon hetero-junction (SHJ) and multi-junction (MJ) solar cells. For Si hetero-junctions, the series connection of either four or five SHJ (4SHJ and 5SHJ, respectively) cells was implemented to obtain the reaction potential required. MJ cells were illuminated by a novel planar solar concentrator that guarantees solar tracking with minimal linear displacements. The three solar-hypochlorite generators were tested under real atmospheric conditions, demonstrating solar-to-chemical conversion efficiencies (SCE) of 9.8% for 4SHJ, 14.2% for 5SHJ and 25.1% for MJ solar cells, respectively. Simulations based on weather databases allowed us to assess efficiencies throughout the entire model year and resulted in specific sodium hypochlorite yearly production rates between 7.2-28 gNaClO cm-2 (referred to the PV surface), depending on the considered PV technology, location, and deployment of electronics converters. The economic viability and competitiveness of solar hypochlorite generators have been investigated and compared with an analog disinfection system deploying ultraviolet lamps. Our study demonstrates the feasibility of off-grid, solar-hypochlorite generators, and points towards the implementation of SHJ solar cells as a reliable technology for stand-alone solar-chemical devices.

Studies on new process procedures in plasma fractionation and industrial scale. III. Removal of precipitates by filtration through a horizontal leaf filter with centrifugal cleaning (Funda filter) as an alternative to tubular-bowl centrifuges.

The possibilities for replacing the time-consuming and labor-intensive centrifugation step in the plasma fractionation process with a filtration technique using filter aids were explored using a Funda filter (pat. Dr. Ing. Hans Müller). The filtration technique proved effective in the separation of the impure plasma fractions. On the other hand, its use in the separation of pure albumin and gamma-globulin fractions appears doubtful.