Cognitive-motor brain–machine interfaces

Brain–machine interfaces (BMIs) open new horizons for the treatment of paralyzed persons, giving hope for the artificial restoration of lost physiological functions. Whereas BMI development has mainly focused on motor rehabilitation, recent studies have suggested that higher cognitive functions can also be deciphered from brain activity, bypassing low level planning and execution functions, and replacing them by computer-controlled effectors. This review describes the new generation of cognitive-motor BMIs, focusing on three BMI types:

• 1.Speech BMI – reconstructing a person’s speech based on the neuronal activity.
• 2.Direct object control – controlling object movement without mimicking the limb movement that would yield the desired object movement.
• 3.Decoding internal processes, such as neuronal representations of sensory information and decision making.

By outlining recent progress in developing these BMI types, we aim to provide a unified view of contemporary research towards the replacement of behavioral outputs of cognitive processes by direct interaction with the brain.     

Fluid–structure interaction simulation of the brain–skull interface for acute subdural haematoma prediction

Traumatic brain injury is a leading cause of disability and mortality. Finite element-based head models are promising tools for enhanced head injury prediction, mitigation and prevention. The reliability of such models depends heavily on adequate representation of the brain–skull interaction. Nevertheless, the brain–skull interface has been largely simplified in previous three-dimensional head models without accounting for the fluid behaviour of the cerebrospinal fluid (CSF) and its mechanical interaction with the brain and skull. In this study, the brain–skull interface in a previously developed head model is modified as a fluid–structure interaction (FSI) approach, in which the CSF is treated on a moving mesh using an arbitrary Lagrangian–Eulerian multi-material formulation and the brain on a deformable mesh using a Lagrangian formulation. The modified model is validated against brain–skull relative displacement and intracranial pressure responses and subsequently imposed to an experimentally determined loading known to cause acute subdural haematoma (ASDH). Compared to the original model, the modified model achieves an improved validation performance in terms of brain–skull relative motion and is able to predict the occurrence of ASDH more accurately, indicating the superiority of the FSI approach for brain–skull interface modelling. The introduction of the FSI approach to represent the fluid behaviour of the CSF and its interaction with the brain and skull is crucial for more accurate head injury predictions.

     

Linear combinations of nonlinear models for predicting human–machine interface forces

 This study presents a computational framework that capitalizes on known human neuromechanical characteristics during limb movements in order to predict human–machine interactions. A parallel–distributed approach, the mixture of nonlinear models, fits the relationship between the measured kinematics and kinetics at the handle of a robot. Each element of the mixture represented the arm and its controller as a feedforward nonlinear model of inverse dynamics plus a linear approximation of musculotendonous impedance. We evaluated this approach with data from experiments where subjects held the handle of a planar manipulandum robot and attempted to make point-to-point reaching movements. We compared the performance to the more conventional approach of a constrained, nonlinear optimization of the parameters. The mixture of nonlinear models accounted for 79±11% (mean ±SD) of the variance in measured force, and force errors were 0.73 ± 0.20% of the maximum exerted force. Solutions were acquired in half the time with a significantly better fit. However, both approaches suffered equally from the simplifying assumptions, namely that the human neuromechanical system consisted of a feedforward controller coupled with linear impedances and a moving state equilibrium. Hence, predictability was best limited to the first half of the movement. The mixture of nonlinear models may be useful in human–machine tasks such as in telerobotics, fly-by-wire vehicles, robotic training, and rehabilitation. Received: 20 October 2000 / Accepted in revised form: 8 May 2001     

Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface

This article considers the features of five electroencephalogram patterns that are most frequently extracted by the independent component analysis when subjects imagine the movement of their hands during the control of a brain–computer interface (BCI). The solution of the EEG inverse problem using the individual geometrical head model shows that the sources of the revealed patterns are located at the bottom of the left and right central sulci, as well as in the left premotor cortex, supplementary motor area, and precuneus. The functional value of the patterns is discussed by comparing the location results with the results of the metaanalysis of the published data that were obtained using a functional magnetic resonance imaging. The source locations are the same for seven healthy subjects and four poststroke patients with subcortical damage location. However, despite the same locations, the two groups of subjects significantly differed in the frequency characteristics of the revealed patterns; in particular, the patients had no clearly pronounced activity in the upper α-band and were characterized by a much lower level of inhibition of rates in the primary somatosensory areas during motor imagery.     

A kinetic framework for tRNA ligase and enforcement of a 2′-phosphate requirement for ligation highlights the design logic of an RNA repair machine

tRNA ligases are essential components of informational and stress-response pathways entailing repair of RNA breaks with 2′,3′-cyclic phosphate and 5′-OH ends. Plant and fungal tRNA ligases comprise three catalytic domains. Phosphodiesterase and kinase modules heal the broken ends to generate the 3′-OH, 2′-PO₄, and 5′-PO₄ required for sealing by the ligase. We exploit RNA substrates with different termini to define rates of individual steps or subsets of steps along the repair pathway of plant ligase AtRNL. The results highlight rate-limiting transactions, how repair is affected by active-site mutations, and how mutations are bypassed by RNA alterations. We gain insights to 2′-PO₄ specificity by showing that AtRNL is deficient in transferring AMP to pRNA_OH to form AppRNA_OH but proficient at sealing pre-adenylylated AppRNA_OH. This strategy for discriminating 2′-PO₄ versus 2′-OH ends provides a quality-control checkpoint to ensure that only purposeful RNA breaks are sealed and to avoid nonspecific “capping” of 5′-PO₄ ends.     

A comparison study of two P300 speller paradigms for brain–computer interface

In this paper, a comparison of two existing P300 spellers is conducted. In the first speller, the visual stimuli of characters are presented in a single character (SC) paradigm and each button corresponding to a character flashes individually in a random order. The second speller is based on a region-based (RB) paradigm. In the first level, all characters are grouped and each button corresponding to a group flashes individually in a random order. Once a group is selected, the characters in it will appear on the flashing buttons of the second level for the selection of desired character. In a spelling experiment involving 12 subjects, higher online accuracy was obtained on the RB paradigm-based P300 speller than the SC paradigm-based P300 speller. Furthermore, we analyzed P300 detection performance, the P300 waveforms and Fisher ratios using the data collected by the two spellers. It was found that the stimuli display paradigm of the RB speller enhances P300 potential and is more suitable for P300 detection.     

Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation

The brain–computer interface (BCI) technology, based on the registration and interpretation of EEG, has recently become one of the most popular developments in neuroscience and psychophysiology. This is due not only to the intended future use of these technologies in many areas of practical human activity, but also to the fact that BCI is a completely new paradigm in psychophysiology, which allows testing hypotheses about the possibilities of the human brain to the development of skills of interaction with the outside world without the mediation of the motor system, i.e., only with the help of voluntary modulation of EEG generators. This paper examines the theoretical and experimental basis, the current state, and the prospects of development of training, communicational, and assisting complexes based on BCI to control them without muscular effort on the basis of decoding mental commands detected in the EEG of patients with severely impaired speech and motor system.     

Optimization of methazolamide-loaded solid lipid nanoparticles for ophthalmic delivery using Box–Behnken design

The purpose of the present study was to optimize methazolamide (MTZ)-loaded solid lipid nanoparticles (SLNs) which were used as topical eye drops by evaluating the relationship between design factors and experimental data. A three factor, three-level Box–Behnken design (BBD) was used for the optimization procedure, choosing the amount of GMS, the amount of phospholipid, the concentration of surfactant as the independent variables. The chosen dependent variables were entrapment efficiency, dosage loading, and particle size. The generated polynomial equations and response surface plots were used to relate the dependent and independent variables. The optimal nanoparticles were formulated with 100?mg GMS, 150?mg phospholipid, and 1% Tween80 and PEG 400 (1:1, w/v). A new formulation was prepared according to these levels. The observed responses were close to the predicted values of the optimized formulation. The particle size was 197.8?±?4.9?nm. The polydispersity index of particle size was 0.239?±?0.01 and the zeta potential was 32.7?±?2.6?mV. The entrapment efficiency and dosage loading were about 68.39% and 2.49%, respectively. Fourier transform infrared spectroscopy (FT-IR) study indicated that the drug was entrapped in nanoparticles. The optimized formulation showed a sustained release followed the Peppas model. MTZ-SLNs showed significant prolonged decreasing intraocular pressure effect comparing with MTZ solution in vivo pharmacodynamics studies. The results of acute eye irritation study indicated that MTZ-SLNs and AZOPT both had no eye irritation. Furthermore, the MTZ-SLNs were suitable to be stored at low temperature (4?°C).     

An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface

Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain–computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system).     

A frequency-weighted method combined with Common Spatial Patterns for electroencephalogram classification in brain–computer interface

Common Spatial Patterns (CSP) has been proven to be a powerful and successful method in the detection of event-related desynchronization (ERD) and ERD based brain–computer interface (BCI). However, frequency optimization combined with CSP has only been investigated by a few groups. In this paper, a frequency-weighted method (FWM) is proposed to optimize the frequency spectrum of surface electroencephalogram (EEG) signals for a two-class mental task classification. This straightforward method computes a weight value for each frequency component according to its importance for the discrimination task and reforms the spectrum with the computed weights. The off-line analysis shows that the proposed method achieves an improvement of about 4% (averaged over 24 datasets) in terms of cross-validation accuracy over the basic CSP.     

Simple assay for adsorption of proteins to the air–water interface

A rapid assay is described, based upon the Marangoni effect, which detects the formation of a denatured-protein film at the air–water interface (AWI) of aqueous samples. This assay requires no more than a 20 µL aliquot of sample, at a protein concentration of no more than1 mg/ml, and it can be performed with any buffer that is used to prepare grids for electron cryo-microscopy (cryo-EM). In addition, this assay provides an easy way to estimate the rate at which a given protein forms such a film at the AWI. Use of this assay is suggested as a way to pre-screen the effect of various additives and chemical modifications that one might use to optimize the preparation of grids, although the final proof of optimization still requires further screening of grids in the electron microscope. In those cases when the assay establishes that a given protein does form a sacrificial, denatured-protein monolayer, it is suggested that subsequent optimization strategies might focus on discovering how to improve the adsorption of native proteins onto that monolayer, rather than to prevent its formation. A second alternative might be to bind such proteins to the surface of rationally designed affinity grids, in order to prevent their diffusion to, and unwanted interaction with, the AWI.     

Analysis of functional brain connections for positive–negative emotions using phase locking value

In this study, we investigate the brain networks during positive and negative emotions for different types of stimulus (audio only, video only and audio + video) in \(\alpha , \beta\), and \(\gamma\) bands in terms of phase locking value, a nonlinear method to study functional connectivity. Results show notable hemispheric lateralization as phase synchronization values between channels are significant and high in right hemisphere for all emotions. Left frontal electrodes are also found to have control over emotion in terms of functional connectivity. Besides significant inter-hemisphere phase locking values are observed between left and right frontal regions, specifically between left anterior frontal and right mid-frontal, inferior-frontal and anterior frontal regions; and also between left and right mid frontal regions. ANOVA analysis for stimulus types show that stimulus types are not separable for emotions having high valence. PLV values are significantly different only for negative emotions or neutral emotions between audio only/video only and audio only/audio + video stimuli. Finding no significant difference between video only and audio + video stimuli is interesting and might be interpreted as that video content is the most effective part of a stimulus.     

Statins--a cure-all for the brain?

'Statins' are 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors - oral cholesterol-lowering drugs that are used to treat hypercholesterolaemia. It is widely accepted that statins have anti-inflammatory effects that are independent of their ability to lower cholesterol. Animal studies and observational clinical studies have indicated that statins might also be effective in treating certain neurological diseases - in particular, multiple sclerosis, Alzheimer's disease and ischaemic stroke. At present, however, results from ongoing prospective, randomized clinical trials are not available.     

Unlocking the secrets to protein–protein interface drug targets using structural mass spectrometry techniques

Protein–protein interactions (PPIs) drive all biologic systems at the subcellular and extracellular level. Changes in the specificity and affinity of these interactions can lead to cellular malfunctions and disease. Consequently, the binding interfaces between interacting protein partners are important drug targets for the next generation of therapies that block such interactions. Unfortunately, protein–protein contact points have proven to be very difficult pharmacological targets because they are hidden within complex 3D interfaces. For the vast majority of characterized binary PPIs, the specific amino acid sequence of their close contact regions remains unknown. There has been an important need for an experimental technology that can rapidly reveal the functionally important contact points of native protein complexes in solution. In this review, experimental techniques employing mass spectrometry to explore protein interaction binding sites are discussed. Hydrogen–deuterium exchange, hydroxyl radical footprinting, crosslinking and the newest technology protein painting are compared and contrasted.     

Screening and selection of media components for lactic acid production using Plackett–Burman design

Plackett–Burman design was used to efficiently select important media components influencing lactic acid production in a two step screening procedure. A total of 36 screening experiments were conducted for studying the effect of various media components such as carbon and nitrogen (simple and complex) sources, minerals/buffering agents and a specific inducer for the production of lactic acid by Lactobacillus plantarum NCIM 2084. The eleven ingredients chosen after the first screening experiments were further screened by a Plackett-Burman design consisting of 12 experiments. Liquefied starch, wheat bran extract, ammonium nitrate, manganese sulphate and sodium acetate were chosen as promising ingredients for further optimisation studies. The highest yield of 41.9?g/l of lactic acid was obtained at the end of 24 hours of fermentation which corresponded to 90% conversion, on the basis of sugar supplied.     

PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine

Background

The majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.

Results

Cross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.

Conclusions

Machine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.