Exploring the Relationship Between EMG Feature Space Characteristics and Control Performance in Machine Learning Myoelectric Control.

In myoelectric machine learning (ML) based control, it has been demonstrated that control performance usually increases with training, but it remains largely unknown which underlying factors govern these improvements. It has been suggested that the increase in performance originates from changes in characteristics of the Electromyography (EMG) patterns, such as separability or repeatability. However, the relation between these EMG metrics and control performance has hardly been studied. We assessed the relation between three common EMG feature space metrics (separability, variability and repeatability) in 20 able bodied participants who learned ML myoelectric control in a virtual task over 15 training blocks on 5 days. We assessed the change in offline and real-time performance, as well as the change of each EMG metric over the training. Subsequently, we assessed the relation between individual EMG metrics and offline and real-time performance via correlation analysis. Last, we tried to predict real-time performance from all EMG metrics via L2-regularized linear regression. Results showed that real-time performance improved with training, but there was no change in offline performance or in any of the EMG metrics. Furthermore, we only found a very low correlation between separability and real-time performance and no correlation between any other EMG metric and real-time performance. Finally, real-time performance could not be successfully predicted from all EMG metrics employing L2-regularized linear regression. We concluded that the three EMG metrics and real-time performance appear to be unrelated.

Tangent space spatial filters for interpretable and efficient Riemannian classification.

OBJECTIVE: Methods based on Riemannian geometry have proven themselves to be good models for decoding in brain-computer interfacing (BCI). However, these methods suffer from the curse of dimensionality and are not possible to deploy in high-density online BCI systems. In addition, the lack of interpretability of Riemannian methods leaves open the possibility that artifacts drive classification performance, which is problematic in the areas where artifactual control is crucial, e.g. neurofeedback and BCIs in patient populations. APPROACH: We rigorously proved the exact equivalence between any linear function on the tangent space and corresponding derived spatial filters. Upon which, we further proposed a set of dimension reduction solutions for Riemannian methods without intensive optimization steps. The proposed pipelines are validated against classic common spatial patterns and tangent space classification using an open-access BCI analysis framework, which contains over seven datasets and 200 subjects in total. At last, the robustness of our framework is verified via visualizing the corresponding spatial patterns. MAIN RESULTS: Proposed spatial filtering methods possess competitive, sometimes even slightly better, performances comparing to classic tangent space classification while reducing the time cost up to 97% in the testing stage. Importantly, the performances of proposed spatial filtering methods converge with using only four to six filter components regardless of the number of channels which is also cross validated by the visualized spatial patterns. These results reveal the possibility of underlying neuronal sources within each recording session. SIGNIFICANCE: Our work promotes the theoretical understanding about Riemannian geometry based BCI classification and allows for more efficient classification as well as the removal of artifact sources from classifiers built on Riemannian methods.

MOABB: trustworthy algorithm benchmarking for BCIs.

OBJECTIVE: Brain-computer interface (BCI) algorithm development has long been hampered by two major issues: small sample sets and a lack of reproducibility. We offer a solution to both of these problems via a software suite that streamlines both the issues of finding and preprocessing data in a reliable manner, as well as that of using a consistent interface for machine learning methods. APPROACH: By building on recent advances in software for signal analysis implemented in the MNE toolkit, and the unified framework for machine learning offered by the scikit-learn project, we offer a system that can improve BCI algorithm development. This system is fully open-source under the BSD licence and available at https://github.com/NeuroTechX/moabb. MAIN RESULTS: We analyze a set of state-of-the-art decoding algorithms across 12 open access datasets, including over 250 subjects. Our results show that even for the best methods, there are datasets which do not show significant improvements, and further that many previously validated methods do not generalize well outside the datasets they were tested on. SIGNIFICANCE: Our analysis confirms that BCI algorithms validated on single datasets are not representative, highlighting the need for more robust validation in the machine learning for BCIs community.

Case series: Slowing alpha rhythm in late-stage ALS patients.

The alpha peak frequency (APF) of the human electroencephalogram (EEG) is a reliable neurophysiological marker for cognitive abilities. In these case series, we document a shift of the APF towards the lower end of the EEG spectrum in two completely locked-in ALS patients. In not completely locked-in ALS patients, the alpha rhythm lies within the common frequency range. We discuss potential implications of this shift for the largely unknown cognitive state of completely locked-in ALS patients.

Task-induced frequency modulation features for brain-computer interfacing.

OBJECTIVE: Task-induced amplitude modulation of neural oscillations is routinely used in brain-computer interfaces (BCIs) for decoding subjects' intents, and underlies some of the most robust and common methods in the field, such as common spatial patterns and Riemannian geometry. While there has been some interest in phase-related features for classification, both techniques usually presuppose that the frequencies of neural oscillations remain stable across various tasks. We investigate here whether features based on task-induced modulation of the frequency of neural oscillations enable decoding of subjects' intents with an accuracy comparable to task-induced amplitude modulation. APPROACH: We compare cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions. We show results with a motor imagery task, a cognitive task, and also preliminary results in patients with amyotrophic lateral sclerosis (ALS), as well as using common spatial patterns and Laplacian filtering. MAIN RESULTS: The frequency features alone do not significantly out-perform traditional amplitude modulation features, and in some cases perform significantly worse. However, across both tasks and pre-processing in healthy subjects the joint space significantly out-performs either the frequency or amplitude features alone. This result only does not hold for ALS patients, for whom the dataset is of insufficient size to draw any statistically significant conclusions. SIGNIFICANCE: Task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigms. This allows more information to be extracted from the EEG signal cheaply and can be used throughout the field of BCIs.

The impact of yeast fermentation on dough matrix properties.

BACKGROUND: Most studies on dough properties are performed on yeastless dough to exclude the complicating, time-dependent effect of yeast. Baker's yeast, however, impacts dough matrix properties during fermentation, probably through the production of primary (CO2 and ethanol) and secondary (glycerol, acetic acid and succinic acid) metabolites. The aim of this study is to obtain a better understanding of the changes in yeasted dough behavior introduced by fermentation, by investigating the impact of yeast fermentation on Farinograph dough consistency, dough spread, Kieffer rig dough extensibility and gluten agglomeration behavior in a fermented dough-batter gluten starch separation system. RESULTS: Results show that fermentation leads to a dough with less flow and lower extensibility that breaks more easily under stress and strain. The dough showed less elastic and more plastic deformation behavior. Gluten agglomerates were smaller for yeasted dough than for the unyeasted control. CONCLUSION: These changes probably have to be attributed to metabolites generated during fermentation. Indeed, organic acids and also ethanol in concentrations produced by yeast were previously shown to have similar effects in yeastless dough. These findings imply the high importance of yeast fermentation metabolites on dough matrix properties in industrial bread production. © 2015 Society of Chemical Industry.

Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis.

Despite decades of research on EEG-based brain-computer interfaces (BCIs) in patients with amyotrophic lateral sclerosis (ALS), there is still little known about how the disease affects the electromagnetic field of the brain. This may be one reason for the present failure of EEG-based BCI paradigms for completely locked-in ALS patients. In order to help understand this failure, we have recorded resting state data from six ALS patients and thirty-two healthy controls to investigate for group differences. While similar studies have been attempted in the past, none have used high-density EEG or tried to distinguish between physiological and non-physiological sources of the EEG. We find an ALS-specific global increase in gamma power (30-90 Hz) that is not specific to the motor cortex, suggesting that the mechanism behind ALS affects non-motor cortical regions even in the absence of comorbid cognitive deficits.

Ethanol at levels produced by Saccharomyces cerevisiae during wheat dough fermentation has a strong impact on dough properties.

Yeast's role in bread making is primarily the fermentative production of carbon dioxide to leaven the dough. Fermentation also impacts dough matrix rheology, thereby affecting the quality of the end product. Surprisingly, the role of ethanol, the other yeast primary metabolite, has been ill studied in this context. Therefore, this study aims to assess the potential impact of ethanol on yeastless dough extensibility and spread and gluten agglomeration at concentrations at which it is produced in fermenting dough, i.e., up to 60 mmol per 100 g of flour. Reduced dough extensibility and dough spread were observed upon incorporation of ethanol in the dough formula, and were more pronounced for a weak than for a strong flour. Uniaxial and biaxial extension tests showed up to 50% decrease in dough extensibility and a dough strength increase of up to 18% for 60 mmol of ethanol/100 g of flour. Ethanol enhanced gluten agglomeration of a weak flour. Sequential extraction of flour in increasing ethanol concentrations showed that better gluten-solvent interaction is a possible explanation for the changed dough behavior.

Succinic acid in levels produced by yeast (Saccharomyces cerevisiae) during fermentation strongly impacts wheat bread dough properties.

Succinic acid (SA) was recently shown to be the major pH determining metabolite produced by yeast during straight-dough fermentation (Jayaram et al., 2013), reaching levels as high as 1.6 mmol/100 g of flour. Here, the impact of such levels of SA (0.8, 1.6 and 2.4 mmol/100 g flour) on yeastless dough properties was investigated. SA decreased the development time and stability of dough significantly. Uniaxial extension tests showed a consistent decrease in dough extensibility upon increasing SA addition. Upon biaxial extension in the presence of 2.4 mmol SA/100 g flour, a dough extensibility decrease of 47-65% and a dough strength increase of 25-40% were seen. While the SA solvent retention capacity of flour increased with increasing SA concentration in the solvent, gluten agglomeration decreased with gluten yield reductions of over 50%. The results suggest that SA leads to swelling and unfolding of gluten proteins, thereby increasing their interaction potential and dough strength, but simultaneously increasing intermolecular electrostatic repulsive forces. These phenomena lead to the reported changes in dough properties. Together, our results establish SA as an important yeast metabolite for dough rheology.

Speech-specific tuning of neurons in human superior temporal gyrus.

How the brain extracts words from auditory signals is an unanswered question. We recorded approximately 150 single and multi-units from the left anterior superior temporal gyrus of a patient during multiple auditory experiments. Against low background activity, 45% of units robustly fired to particular spoken words with little or no response to pure tones, noise-vocoded speech, or environmental sounds. Many units were tuned to complex but specific sets of phonemes, which were influenced by local context but invariant to speaker, and suppressed during self-produced speech. The firing of several units to specific visual letters was correlated with their response to the corresponding auditory phonemes, providing the first direct neural evidence for phonological recoding during reading. Maximal decoding of individual phonemes and words identities was attained using firing rates from approximately 5 neurons within 200 ms after word onset. Thus, neurons in human superior temporal gyrus use sparse spatially organized population encoding of complex acoustic-phonetic features to help recognize auditory and visual words.

Mapping of Saccharomyces cerevisiae metabolites in fermenting wheat straight-dough reveals succinic acid as pH-determining factor.

Fermenting yeast does not merely cause dough leavening, but also contributes to the bread aroma and might alter dough rheology. Here, the yeast carbon metabolism was mapped during bread straight-dough fermentation. The concentration of most metabolites changed quasi linearly as a function of fermentation time. Ethanol and carbon dioxide concentrations reached up to 60 mmol/100g flour. Interestingly, high levels of glycerol (up to 10 mmol/100g flour) and succinic acid (up to 1.6 mmol/100g flour) were produced during dough fermentation. Further tests showed that, contrary to current belief, the pH decrease in fermenting dough is primarily caused by the production of succinic acid by the yeast instead of carbon dioxide dissolution or bacterial organic acids. Together, our results provide a comprehensive overview of metabolite production during dough fermentation and yield insight into the importance of some of these metabolites for dough properties.

Gastroprotective Properties of Karanjin from Karanja (Pongamia pinnata) Seeds; Role as Antioxidant and H, K-ATPase Inhibitor.

Plant extracts are the most attractive sources of newer drugs and have been shown to produce promising results for the treatment of gastric ulcers. Karanjin, a furano-flavonoid has been evaluated for anti-ulcerogenic property by employing adult male albino rats. Karanjin (>95% pure) was administered to these rats in two different concentrations, that is, 10 and 20 mg kg(-1) b.w. Ulcers were induced in the experimental animals by swim and ethanol stress. Serum, stomach and liver-tissue homogenates were assessed for biochemical parameters. Karanjin inhibited 50 and 74% of ulcers induced by swim stress at 10 and 20 mg kg(-1) b.w., respectively. Gastric mucin was protected up to 85% in case of swim stress, whereas only 47% mucin recovery was seen in ethanol stress induced ulcers. H(+), K(+)-ATPase activity, which was increased 2-fold in ulcer conditions, was normalized by Karanjin in both swim/ethanol stress-induced ulcer models. Karanjin could inhibit oxidative stress as evidenced by the normalization of lipid peroxidation and antioxidant enzyme (i.e., catalase, peroxidase and superoxide dismutase) levels. Karanjin at concentrations of 20 mg kg(-1) b.w., when administered orally for 14 days, did not indicate any lethal effects. There were no significant differences in total protein, serum glutamate pyruvate transaminase, serum glutamate oxaloacetate transaminase and alkaline phosphatase between normal and Karanjin-treated rats indicating no adverse effect on major organs. During treatment schedule, animals remained as healthy as control animals with normal food and water intake and body weight gain.