Reducing the sizes of low dimensional materials leads to dramatic increase in the portion of surface/interface atoms. The properties of a solid are essentially controlled by related surface/interface ...energies. Although such changes are believed to dominate behaviors of nanoscale structures, little experience or intuition for the expected phenomena, especially for the size-dependence of the energies and their practical implications, are modeled analytically. In this contribution, the classic thermodynamics as a powerful traditional theoretical tool is used to model different bulk interface energies and the corresponding size dependences. During the modeling, an emphasis on size dependences of the interface energies is given, which is induced by size dependence of coherent energy of atoms within nanocrystals. It is found that solid–vapor interface energy, liquid–vapor interface energy, solid–liquid interface energy, and solid–solid interface energy of nanoparticles and thin films fall as their diameters or thickness decrease to several nanometers while the solid–vapor interface energy ratio between different facets is size-independent and equals to the corresponding bulk value. The predictions of the established analytic models without any free parameters, such as size and temperature, dependences of these four kinds of interface energies and related surface stress, correspond to experimental or other theoretical results. The above established models are suitable for low-dimensional materials with different dimensions and different chemical bond natures. Moreover, several related applications in the fields of nanophase transitions, nanocrystal growth, and self-diffusion of liquids are provided.
Neural electrodes enable the recording and stimulation of bioelectrical activity in the nervous system. This technology provides neuroscientists with the means to probe the functionality of neural ...circuitry in both health and disease. In addition, neural electrodes can deliver therapeutic stimulation for the relief of debilitating symptoms associated with neurological disorders such as Parkinson’s disease and may serve as the basis for the restoration of sensory perception through peripheral nerve and brain regions after disease or injury. Lastly, microscale neural electrodes recording signals associated with volitional movement in paralyzed individuals can be decoded for controlling external devices and prosthetic limbs or driving the stimulation of paralyzed muscles for functional movements. In spite of the promise of neural electrodes for a range of applications, chronic performance remains a goal for long-term basic science studies, as well as clinical applications. New perspectives and opportunities from fields including tissue biomechanics, materials science, and biological mechanisms of inflammation and neurodegeneration are critical to advances in neural electrode technology. This Special Issue will address the state-of-the-art knowledge and emerging opportunities for the development and demonstration of advanced neural electrodes.
Brain–computer interfaces (BCIs) are a form of technology that read a user’s neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide ...range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as “BCI illiteracy,” and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.
In a previous study we demonstrated continuous translation, orientation and one-dimensional grasping control of a prosthetic limb (seven degrees of freedom) by a human subject with tetraplegia using ...a brain-machine interface (BMI). The current study, in the same subject, immediately followed the previous work and expanded the scope of the control signal by also extracting hand-shape commands from the two 96-channel intracortical electrode arrays implanted in the subject's left motor cortex.
Four new control signals, dictating prosthetic hand shape, replaced the one-dimensional grasping in the previous study, allowing the subject to control the prosthetic limb with ten degrees of freedom (three-dimensional (3D) translation, 3D orientation, four-dimensional hand shaping) simultaneously.
Robust neural tuning to hand shaping was found, leading to ten-dimensional (10D) performance well above chance levels in all tests. Neural unit preferred directions were broadly distributed through the 10D space, with the majority of units significantly tuned to all ten dimensions, instead of being restricted to isolated domains (e.g. translation, orientation or hand shape). The addition of hand shaping emphasized object-interaction behavior. A fundamental component of BMIs is the calibration used to associate neural activity to intended movement. We found that the presence of an object during calibration enhanced successful shaping of the prosthetic hand as it closed around the object during grasping.
Our results show that individual motor cortical neurons encode many parameters of movement, that object interaction is an important factor when extracting these signals, and that high-dimensional operation of prosthetic devices can be achieved with simple decoding algorithms. ClinicalTrials.gov Identifier: NCT01364480.
The presented 16-channel <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-modulated neural analog-to-digital converter (ADC) exhibits tolerance to input dc offsets of ...any value, up to the supply voltage. It employs a dynamic differential-difference comparator architecture with a super-<inline-formula> <tex-math notation="LaTeX">\text{G}\Omega </tex-math></inline-formula> input impedance ensuring negligible gate-leakage current and well-matched differential inputs resulting in more than 78 dB of rejection of common-mode signals and artifacts. The all-digital nature of the presented <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-ADC enables sampling of input signals at high oversampling ratios (OSRs) making the front-end immune to stimulation artifacts with differential amplitudes up to a limit that is scalable by the OSR (e.g., 10 mV PP at OSR <inline-formula> <tex-math notation="LaTeX">=\,\,10 </tex-math></inline-formula> k). Moreover, it allows the <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-ADC to linearly scale down the power consumption required by the application's recording bandwidth. The oversampled <inline-formula> <tex-math notation="LaTeX">\Delta </tex-math></inline-formula>-ADC achieves an effective number of bits (ENOB) of 9.7-bit and 2.6-<inline-formula> <tex-math notation="LaTeX">\mu \text{V}_{\mathrm {RMS}} </tex-math></inline-formula> integrated input-referred noise over 1 Hz to 500-Hz bandwidth. It uses no large passives or statically biased circuits, such as operational amplifiers (Opamps) saving both channel area (0.011 mm 2 ) and power consumption (0.99 <inline-formula> <tex-math notation="LaTeX">\mu \text{W} </tex-math></inline-formula>). Experimentally measured results validate the key features of the design and include in vivo recordings in freely moving guinea pigs. The fabricated prototype system-on-a-chip (SoC) hosts an array of 16-channel neural-ADC with in-channel digitally programmable high-compliance current-mode biphasic stimulators as well as wireless circuitry for data communication and power/command reception.
A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common noninvasive BCI modality, electroencephalogram (EEG), is sensitive to ...noise/artifact and suffers between-subject/within-subject nonstationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This article reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications-motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks-are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the article, which may point to future research directions.
Objective: This paper tackles the problem of transfer learning in the context of electroencephalogram (EEG)-based brain-computer interface (BCI) classification. In particular, the problems of ...cross-session and cross-subject classification are considered. These problems concern the ability to use data from previous sessions or from a database of past users to calibrate and initialize the classifier, allowing a calibration-less BCI mode of operation. Methods: Data are represented using spatial covariance matrices of the EEG signals, exploiting the recent successful techniques based on the Riemannian geometry of the manifold of symmetric positive definite (SPD) matrices. Cross-session and cross-subject classification can be difficult, due to the many changes intervening between sessions and between subjects, including physiological, environmental, as well as instrumental changes. Here, we propose to affine transform the covariance matrices of every session/subject in order to center them with respect to a reference covariance matrix, making data from different sessions/subjects comparable. Then, classification is performed both using a standard minimum distance to mean classifier, and through a probabilistic classifier recently developed in the literature, based on a density function (mixture of Riemannian Gaussian distributions) defined on the SPD manifold. Results: The improvements in terms of classification performances achieved by introducing the affine transformation are documented with the analysis of two BCI datasets. Conclusion and significance: Hence, we make, through the affine transformation proposed, data from different sessions and subject comparable, providing a significant improvement in the BCI transfer learning problem.
Several studies have shown that users of immersive virtual reality can feel high levels of embodiment in self avatars that have different morphological proportions th a n th ose of their actual ...bodies . Deformed and unrealistic morphological modifications are accepted by embodied users, underlying the adaptability of one's mental map of their body (body schema) in response to incomi ng sensory feedback. Before initiating a motor action, the brain uses the body schema to plan and sequence the necessary movements. Therefore, embodiment in a self avatar with a different mo rphology, such as one with deformed proportions, could lead to changes in motor planning and execution. In this study, we aimed to measure the effects on movement planning and execution of embodying a self avatar with an enlarged lower leg on one side. Thirty participants embodied an avatar without any deformations, and with an enlarged dominant or non domin ant leg, in randomized order. Two different levels of embodiment were induced, using synchronous or asynchronous visuotactile stimuli. In each condition , participants performed a gait initiation task. Their center of mass and center of pressure were measured, and the margin of stability (MoS) was computed from these values. Their perceived level of embodiment was also measured, using a validated questionnaire. Results show no significant changes on the biom e chenical variables related to dynamic stability Embodiment scores decreased with asynchronous stimuli, without impacting the measures related to stability. The body schema may not have been impacted by the larger virtual leg . Ho wever, d eforming the self avatar's morphology could have important implications when addressing individuals with impaired physical mobility by subtly influencing action execution dur ing a rehabilitation protocol.
Although promising from numerous applications, current brain-computer interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the ...non-stationarity of electroencephalographic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.
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