Personalized neuroprosthetics

Decades of technological developments have populated the field of neuroprosthetics with myriad replacement strategies, neuromodulation therapies, and rehabilitation procedures to improve the quality of life for individuals with neuromotor disorders. Despite the few but impressive clinical successes, and multiple breakthroughs in animal models, neuroprosthetic technologies remain mainly confined to sophisticated laboratory environments. We summarize the core principles and latest achievements in neuroprosthetics, but also address the challenges that lie along the path toward clinical fruition. We propose a pragmatic framework to personalise neurotechnologies and rehabilitation for patient-specific impairments to achieve the timely dissemination of neuroprosthetic medicine

Asynchronous Non-Invasive Brain-Actuated Control of an Intelligent Wheelchair

In this paper we present further results of our asynchronous and non-invasive BMI for the continuous control of an intelligent wheelchair. Three subjects participated in two experiments where they steered the wheelchair spontaneously, without any external cue. To do so the users learn to voluntary modulate EEG oscillatory rhythms by executing three mental tasks (i.e., mental imagery) that are associated to different steering commands. Importantly, we implement shared control techniques between the BMI and the intelligent wheelchair to assist the subject in the driving task. The results show that the three subjects could achieve a significant level of mental control, even if far from optimal, to drive an intelligent wheelchair

Brain-Computer Interfaces for HCI and Games

In this workshop we study the research themes and the state-of-the-art of brain-computer interaction. Braincomputer interface research has seen much progress in the medical domain, for example for prosthesis control or as biofeedback therapy for the treatment of neurological disorders. Here, however, we look at brain-computer interaction especially as it applies to research in Human-Computer Interaction (HCI). Through this workshop and continuing discussions, we aim to define research approaches and applications that apply to disabled and able-bodied users across a variety of real-world usage scenarios. Entertainment and game design is one of the application areas that will be considered

Brain-Machine Interfaces through Control of Electroencephalographic Signals and Vibrotactile Feedback

A Brain-Computer Interface (BCI) allow direct expression of its user�s will by interpreting signals which directly reflect the brain�s activity, thus bypassing the natural efferent channels (nerves and muscles). To be correctly mastered, it is needed that this artificial efferent channel is complemented by an artificial feedback, which continuously informs the user about the current state (in the same way as proprioceptors give a feedback about joint angle and muscular tension). This feedback is usually delivered through the visual channel. We explored the benefits of vibrotactile feedback during users� training and control of EEG-based BCI applications. A protocol for delivering vibrotactile feedback, including specific hardware and software arrangements, was specified and implemented. Thirteen subjects participated in an experiment where the feedback of the BCI system was delivered either through a visual display, or through a vibrotactile display, while they performed a virtual navigation task. Attention to the task was probed by presenting visual cues that the subjects had to describe afterwards. When compared with visual feedback, the use of tactile feedback did not decrease BCI control performance; on the other side, it improved the capacity of subjects to concentrate on the requested (visual) task. During experiments, vibrotactile feedback felt (after some training) more natural. This study indicated that the vibrotactile channel can function as a valuable feedback modality in the context of BCI applications. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task

Study of USH1 Splicing Variants through Minigenes and Transcript Analysis from Nasal Epithelial Cells

Usher syndrome type I (USH1) is an autosomal recessive disorder characterized by congenital profound deafness, vestibular areflexia and prepubertal retinitis pigmentosa. The first purpose of this study was to determine the pathologic nature of eighteen USH1 putative splicing variants found in our series and their effect in the splicing process by minigene assays. These variants were selected according to bioinformatic analysis. The second aim was to analyze the USH1 transcripts, obtained from nasal epithelial cells samples of our patients, in order to corroborate the observed effect of mutations by minigenes in patient’s tissues. The last objective was to evaluate the nasal ciliary beat frequency in patients with USH1 and compare it with control subjects. In silico analysis were performed using four bioinformatic programs: NNSplice, Human Splicing Finder, NetGene2 and Spliceview. Afterward, minigenes based on the pSPL3 vector were used to investigate the implication of selected changes in the mRNA processing. To observe the effect of mutations in the patient’s tissues, RNA was extracted from nasal epithelial cells and RT-PCR analyses were performed. Four MYO7A (c.470G>A, c.1342_1343delAG, c.5856G>A and c.3652G>A), three CDH23 (c.2289+1G>A, c.6049G>A and c.8722+1delG) and one PCDH15 (c.3717+2dupTT) variants were observed to affect the splicing process by minigene assays and/or transcripts analysis obtained from nasal cells. Based on our results, minigenes are a good approach to determine the implication of identified variants in the mRNA processing, and the analysis of RNA obtained from nasal epithelial cells is an alternative method to discriminate neutral Usher variants from those with a pathogenic effect on the splicing process. In addition, we could observe that the nasal ciliated epithelium of USH1 patients shows a lower ciliary beat frequency than control subjects

Vibrotactile Feedback in the Context of Mu-Rhythm based BCI

Brain-Computer Interfaces (BCIs) need an uninterrupted flow of feedback to the user, which is usually delivered through the visual channel. Our aim is to explore the benefits of vibrotactile feedback during users� training and control of EEG-based BCI applications. An experimental setup for delivery of vibrotactile feedback, including specific hardware and software arrangements, was specified. We compared vibrotactile and visual feedback, addressing the performance in presence of a complex visual task on the same (visual) or different (tactile) sensory channel. The preliminary experimental setup included a simulated BCI control. in which all parts reflected the computational and actuation process of an actual BCI, except the souce, which was simulated using a �noisy� PC mouse. Results indicated that the vibrotactile channel can function as a valuable feedback modality with reliability comparable to the classical visual feedback. Advantages of using a vibrotactile feedback emerged when the visual channel was highly loaded by a complex task

High-Resolution EEG Techniques for Brain-Computer Interface Applications

High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method

Alkaline Phosphatases: Structure, substrate specificity and functional relatedness to other members of a large superfamily of enzymes

Our knowledge of the structure and function of alkaline phosphatases has increased greatly in recent years. The crystal structure of the human placental isozyme has enabled us to probe salient features of the mammalian enzymes that differ from those of the bacterial enzymes. The availability of knockout mice deficient in each of the murine alkaline phosphatase isozymes has also given deep insights into their in vivo role. This has been particularly true for probing the biological role of bone alkaline phosphatase during skeletal mineralization. Due to space constraints this mini-review focuses exclusively on structural and functional features of mammalian alkaline phosphatases as identified by crystallography and probed by site-directed mutagenesis and kinetic analysis. An emphasis is also placed on the substrate specificity of alkaline phosphatases, their catalytic properties as phosphohydrolases as well as phosphodiesterases and their structural and functional relatedness to a large superfamily of enzymes that includes nucleotide pyrophosphatase/phosphodiesterase

A Genetic Basis for Mechanosensory Traits in Humans

Hearing and touch are genetically related, and people with excellent hearing are more likely to have a fine sense of touch and vice versa

Prospects on Brain-Machine Interfaces for Space System Control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical systems. This paper describes the state of the art of non-invasive BMIs and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation