Towards a Cure for BCI Illiteracy

Brain–Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. One of the biggest challenges in BCI research is to understand and solve the problem of “BCI Illiteracy”, which is that BCI control does not work for a non-negligible portion of users (estimated 15 to 30%). Here, we investigate the illiteracy problem in BCI systems which are based on the modulation of sensorimotor rhythms. In this paper, a sophisticated adaptation scheme is presented which guides the user from an initial subject-independent classifier that operates on simple features to a subject-optimized state-of-the-art classifier within one session while the user interacts the whole time with the same feedback application. While initial runs use supervised adaptation methods for robust co-adaptive learning of user and machine, final runs use unsupervised adaptation and therefore provide an unbiased measure of BCI performance. Using this approach, which does not involve any offline calibration measurement, good performance was obtained by good BCI participants (also one novice) after 3–6 min of adaptation. More importantly, the use of machine learning techniques allowed users who were unable to achieve successful feedback before to gain significant control over the BCI system. In particular, one participant had no peak of the sensory motor idle rhythm in the beginning of the experiment, but could develop such peak during the course of the session (and use voluntary modulation of its amplitude to control the feedback application)

Polarized Neutron Matter: A Lowest Order Constrained Variational Approach

In this paper, we calculate some of the polarized neutron matter properties, using the lowest order constrained variational method with the $AV_{18}$ potential and employing a microscopic point of view. A comparison is also made between our results and those of other many-body techniques.Comment: 23 pages, 8 figure

Cross-cutting Perspective Freshwater

One singularity of northwestern Europe (NWE) is that severe droughts are rare events in the region and water scarcity has hardly been experienced in its history. The DROP pilot sites are not exceptions to this context. Although the lack of a drought history in wet areas can explain why drought and water scarcity are not necessarily the focus of (if ever considered in) river basin management plans, it must be noted that freshwater availability for drinking water provision remains a priority stake in both quantitative and qualitative aspects. Providing a reliable and safe supply of drinking water may thus be a leading entryway to the development of drought risk awareness and drought adaptation measures in a river basin. When such essential resource is threatened and the competition for water among users increases, there is a good chance that reflections and changes will be triggered. Water use conflicts and drinking water supply threats may arise due to increased water demand, but also due to decreased water availability. The later may occur because of natural climate variability, i.e., drier years than average, or as the result of the impact of climate change on local water resources. Climate change awareness is then an important asset to manage water availability. Where climate change awareness is low and adaptation measures are basically inexistent, social and political responses to drought adaptation may be slow and inefficient. However, even in those cases where climate change awareness is still low in general society, water authorities and other stakeholders are conscious that water demand tends to intensify with population and economic growth, rendering water scarcity conceivable and even foreseeable. Freshwater availability for drinking water supply is therefore an issue that can motivate the introduction of drought and water scarcity risks into the political and public agenda , even in “ drought-scarce ” regions. This chapter highlights the links between drought governance and the vulnerability of freshwater for drinking water supply, with a focus on drought adaptation. The main issues presented here are illustrated with how freshwater issues are managed in the DROP project cases with a particular focus on the two “ freshwater reservoir ” pilot sites: the Arzal dam in Brittany France (see Chap. 6 ) and the Eifel-Rur in Germany (see Chap. 4 ). Those two cases deal with reservoir management not only for drinking water supply (Fig. 11.1 ) but also for other uses, with various priority sets

Sensorimotor functional connectivity: A neurophysiological factor related to BCI performance

Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

A comparison of univariate, vector, bilinear autoregressive, and band power features for brain–computer interfaces

Selecting suitable feature types is crucial to obtain good overall brain–computer interface performance. Popular feature types include logarithmic band power (logBP), autoregressive (AR) parameters, time-domain parameters, and wavelet-based methods. In this study, we focused on different variants of AR models and compare performance with logBP features. In particular, we analyzed univariate, vector, and bilinear AR models. We used four-class motor imagery data from nine healthy users over two sessions. We used the first session to optimize parameters such as model order and frequency bands. We then evaluated optimized feature extraction methods on the unseen second session. We found that band power yields significantly higher classification accuracies than AR methods. However, we did not update the bias of the classifiers for the second session in our analysis procedure. When updating the bias at the beginning of a new session, we found no significant differences between all methods anymore. Furthermore, our results indicate that subject-specific optimization is not better than globally optimized parameters. The comparison within the AR methods showed that the vector model is significantly better than both univariate and bilinear variants. Finally, adding the prediction error variance to the feature space significantly improved classification results

Improving motor imagery classification during induced motor perturbations

Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems

Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems

Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions

Isolation and Quantification of miRNA from the Biomolecular Corona on Mesoporous Silica Nanoparticles

[EN] To understand the factors that control the formation of the biomolecular corona, a systematic study of the adsorption of several miRNAs shown to be important in prostate cancer on amine-functionalized mesoporous silica nanoparticles (MSN-NH2) has been performed. Process parameters including miRNA type, nanoparticle concentration, incubation temperature and incubation time were investigated, as well as the potential competition for adsorption between different miRNA molecules. The influence of proteins and particle PEGylation on miRNA adsorption were also explored. We found that low particle concentrations and physiological temperature both led to increased miRNA adsorption. Adsorption of miRNA was also higher when proteins were present in the same solution; reducing or preventing protein adsorption by PEGylating the MSNs hindered adsorption. Finally, the amount of miRNA adsorbed from human serum by MSN-NH2 was compared to a commercial miRNA purification kit (TaqMan(R), Life Technologies, Carlsbad, CA, USA). MSN-NH2 adsorbed six times as much miRNA as the commercial kit, demonstrating higher sensitivity to subtle up- and downregulation of circulating miRNA in the blood of patients.This research was funded by the Spanish Ministry of Economy and Competitiveness (project PID2019-111436RB-C21), and the Generalitat Valenciana (project PROMETEO/2017/060).Vidaurre Agut, CM.; Rivero-Buceta, EM.; Landry, CC.; Botella Asuncion, P. (2021). Isolation and Quantification of miRNA from the Biomolecular Corona on Mesoporous Silica Nanoparticles. Nanomaterials. 11(5):1-11. https://doi.org/10.3390/nano11051196S11111