Manipulating Single-Trial Motor Performance in Chronic Stroke Patients by Closed-Loop Brain State Interaction.

Motor impaired patients performing repetitive motor tasks often reveal large single-trial performance variations. Based on a data-driven framework, we extracted robust oscillatory brain states from pre-trial intervals, which are predictive for the upcoming motor performance on the level of single trials. Based on the brain state estimate, i.e. whether the brain state predicts a good or bad upcoming performance, we implemented a novel gating strategy for the start of trials by selecting specifically suitable or unsuitable trial starting time points. In a pilot study with four chronic stroke patients with hand motor impairments, we conducted a total of 41 sessions. After few initial calibration sessions, patients completed approximately 15 hours of effective hand motor training during eight online sessions using the gating strategy. Patients' reaction times were significantly reduced for suitable trials compared to unsuitable trials and shorter overall trial durations under suitable states were found in two patients. Overall, this successful proof-of-concept pilot study motivates to transfer this closed-loop training framework to a clinical study and to other application fields, such as cognitive rehabilitation, sport sciences or systems neuroscience.

Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods.

Many cognitive, sensory and motor processes have correlates in oscillatory neural source activity, which is embedded as a subspace in the recorded brain signals. Decoding such processes from noisy magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires data-driven analysis methods. The objective evaluation of such decoding algorithms on experimental raw signals, however, is a challenge: the amount of available M/EEG data typically is limited, labels can be unreliable, and raw signals often are contaminated with artifacts. To overcome some of these problems, simulation frameworks have been introduced which support the development of data-driven decoding algorithms and their benchmarking. For generating artificial brain signals, however, most of the existing frameworks make strong and partially unrealistic assumptions about brain activity. This limits the generalization of results observed in the simulation to real-world scenarios. In the present contribution, we show how to overcome several shortcomings of existing simulation frameworks. We propose a versatile alternative, which allows for an objective evaluation and benchmarking of novel decoding algorithms using real neural signals. It allows to generate comparatively large datasets with labels being deterministically recoverable from the arbitrary M/EEG recordings. A novel idea to generate these labels is central to this framework: we determine a subspace of the true M/EEG recordings and utilize it to derive novel labels. These labels contain realistic information about the oscillatory activity of some underlying neural sources. For two categories of subspace-defining methods, we showcase how such labels can be obtained-either by an exclusively data-driven approach (independent component analysis-ICA), or by a method exploiting additional anatomical constraints (minimum norm estimates-MNE). We term our framework post-hoc labeling of M/EEG recordings. To support the adoption of the framework by practitioners, we have exemplified its use by benchmarking three standard decoding methods-i.e., common spatial patterns (CSP), source power-comodulation (SPoC), and convolutional neural networks (ConvNets)-wrt. Varied dataset sizes, label noise, and label variability. Source code and data are made available to the reader for facilitating the application of our post-hoc labeling framework.

Mining Within-Trial Oscillatory Brain Dynamics to Address the Variability of Optimized Spatial Filters.

Data-driven spatial filtering algorithms optimize scores, such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for the filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and involved hyperparameters. This leads to highly variable solutions and impedes the selection of a suitable candidate for, e.g., neurotechnological applications. Fostering component introspection, we propose to embrace this variability by condensing the functional signatures of a large set of oscillatory components into homogeneous clusters, each representing specific within-trial envelope dynamics. The proposed method is exemplified by and evaluated on a complex hand force task with a rich within-trial structure. Based on electroencephalography data of 18 healthy subjects, we found that the components' distinct temporal envelope dynamics are highly subject-specific. On average, we obtained seven clusters per subject, which were strictly confined regarding their underlying frequency bands. As the analysis method is not limited to a specific spatial filtering algorithm, it could be utilized for a wide range of neurotechnological applications, e.g., to select and monitor functionally relevant features for brain-computer interface protocols in stroke rehabilitation.

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems : Guidelines Derived from Simulation and Real-World Data.

We report on novel supervised algorithms for single-trial brain state decoding. Their reliability and robustness are essential to efficiently perform neurotechnological applications in closed-loop. When brain activity is assessed by multichannel recordings, spatial filters computed by the source power comodulation (SPoC) algorithm allow identifying oscillatory subspaces. They regress to a known continuous trial-wise variable reflecting, e.g. stimulus characteristics, cognitive processing or behavior. In small dataset scenarios, this supervised method tends to overfit to its training data as the involved recordings via electroencephalogram (EEG), magnetoencephalogram or local field potentials generally provide a low signal-to-noise ratio. To improve upon this, we propose and characterize three types of regularization techniques for SPoC: approaches using Tikhonov regularization (which requires model selection via cross-validation), combinations of Tikhonov regularization and covariance matrix normalization as well as strategies exploiting analytical covariance matrix shrinkage. All proposed techniques were evaluated both in a novel simulation framework and on real-world data. Based on the simulation findings, we saw our expectations fulfilled, that SPoC regularization generally reveals the largest benefit for small training sets and under severe label noise conditions. Relevant for practitioners, we derived operating ranges of regularization hyperparameters for cross-validation based approaches and offer open source code. Evaluating all methods additionally on real-world data, we observed an improved regression performance mainly for datasets from subjects with initially poor performance. With this proof-of-concept paper, we provided a generalizable regularization framework for SPoC which may serve as a starting point for implementing advanced techniques in the future.

Pre-Trial EEG-Based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task.

We propose a framework for building electrophysiological predictors of single-trial motor performance variations, exemplified for SVIPT, a sequential isometric force control task suitable for hand motor rehabilitation after stroke. Electroencephalogram (EEG) data of 20 subjects with mean age of 53 years was recorded prior to and during 400 trials of SVIPT. They were executed within a single session with the non-dominant left hand, while receiving continuous visual feedback of the produced force trajectories. The behavioral data showed strong trial-by-trial performance variations for five clinically relevant metrics, which accounted for reaction time as well as for the smoothness and precision of the produced force trajectory. 18 out of 20 tested subjects remained after preprocessing and entered offline analysis. Source Power Comodulation (SPoC) was applied on EEG data of a short time interval prior to the start of each SVIPT trial. For 11 subjects, SPoC revealed robust oscillatory EEG subspace components, whose bandpower activity are predictive for the performance of the upcoming trial. Since SPoC may overfit to non-informative subspaces, we propose to apply three selection criteria accounting for the meaningfulness of the features. Across all subjects, the obtained components were spread along the frequency spectrum and showed a variety of spatial activity patterns. Those containing the highest level of predictive information resided in and close to the alpha band. Their spatial patterns resemble topologies reported for visual attention processes as well as those of imagined or executed hand motor tasks. In summary, we identified subject-specific single predictors that explain up to 36% of the performance fluctuations and may serve for enhancing neuroergonomics of motor rehabilitation scenarios.

Measuring Local Viscosities near Plasma Membranes of Living Cells with Photonic Force Microscopy.

The molecular processes of particle binding and endocytosis are influenced by the locally changing mobility of the particle nearby the plasma membrane of a living cell. However, it is unclear how the particle's hydrodynamic drag and momentum vary locally and how they are mechanically transferred to the cell. We have measured the thermal fluctuations of a 1 µm-sized polystyrene sphere, which was placed in defined distances to plasma membranes of various cell types by using an optical trap and fast three-dimensional (3D) interferometric particle tracking. From the particle position fluctuations on a 30 µs timescale, we determined the distance-dependent change of the viscous drag in directions perpendicular and parallel to the cell membrane. Measurements on macrophages, adenocarcinoma cells, and epithelial cells revealed a significantly longer hydrodynamic coupling length of the particle to the membrane than those measured at giant unilamellar vesicles (GUVs) or a plane glass interface. In contrast to GUVs, there is also a strong increase in friction and in mean first passage time normal to the cell membrane. This hydrodynamic coupling transfers a different amount of momentum to the interior of living cells and might serve as an ultra-soft stimulus triggering further reactions.

Probing meaningfulness of oscillatory EEG components with bootstrapping, label noise and reduced training sets.

As oscillatory components of the Electroencephalogram (EEG) and other electrophysiological signals may co-modulate in power with a target variable of interest (e.g. reaction time), data-driven supervised methods have been developed to automatically identify such components based on labeled example trials. Under conditions of challenging signal-to-noise ratio, high-dimensional data and small training sets, however, these methods may overfit to meaningless solutions. Examples are spatial filtering methods like Common Spatial Patterns (CSP) and Source Power Comodulation (SPoC). It is difficult for the practitioner to tell apart meaningful from arbitrary, random components. We propose three approaches to probe the robustness of extracted oscillatory components and show their application to both, simulated and EEG data recorded during a visually cued hand motor reaction time task.

Induced phagocytic particle uptake into a giant unilamellar vesicle.

Phagocytosis, the uptake and ingestion of solid particles into living cells, is a central mechanism of our immune system. Due to the complexity of the uptake mechanism, the different forces involved in this process are only partly understood. Therefore the usage of a giant unilamellar vesicle (GUV) as the simplest biomimetic model for a cell allows one to investigate the influence of the lipid membrane on the energetics of the uptake process. Here, a photonic force microscope (PFM) is used to approach an optically trapped 1 µm latex bead to an immobilized GUV to finally insert the particle into the GUV. By analysing the mean displacement and the position fluctuations of the trapped particle during the uptake process in 3D with nanometre precision, we are able to record force and energy profiles, as well as changes in the viscous drag and the stiffness. After observing a global followed by a local deformation of the GUV, we measured uptake energies of 2000 kT to 5500 kT and uptake forces of 4 pN to 16 pN for Egg-PC GUVs with sizes of 18-26 µm and varying membrane tension. The measured energy profiles, which are compared to a Helfrich energy model for local and global deformation, show good coincidence with the theoretical results. Our proof-of-principle study opens the door to a large number of similar experiments with GUVs containing more biochemical components and complexity. This bottom-up strategy should allow for a better understanding of the physics of phagocytosis.