A cryptography-based approach for movement decoding.
Ontology highlight
ABSTRACT:
SUBMITTER: Dyer EL
PROVIDER: S-EPMC8376093 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:An efficient cryptography scheme is proposed based on continuous-variable quantum neural network (CV-QNN), in which a specified CV-QNN model is introduced for designing the quantum cryptography algorithm. It indicates an approach to design a quantum neural cryptosystem which contains the processes of key generation, encryption and decryption. Security analysis demonstrates that our scheme is security. Several simulation experiments are performed on the Strawberry Fields platform for processing the classical data "Quantum Cryptography" with CV-QNN to describe the feasibility of our method. Three sets of representative experiments are presented and the second experimental results confirm that our scheme can correctly and effectively encrypt and decrypt data with the optimal learning rate 8e?-?2 regardless of classical or quantum data, and better performance can be achieved with the method of learning rate adaption (where increase factor R1?=?2, decrease factor R2?=?0.8). Indeed, the scheme with learning rate adaption can shorten the encryption and decryption time according to the simulation results presented in Figure 12. It can be considered as a valid quantum cryptography scheme and has a potential application on quantum devices.
| S-EPMC7005808 | biostudies-literature
Project description:How do we understand the intentions of other people? There has been a longstanding controversy over whether it is possible to understand others' intentions by simply observing their movements. Here, we show that indeed movement kinematics can form the basis for intention detection. By combining kinematics and psychophysical methods with classification and regression tree (CART) modeling, we found that observers utilized a subset of discriminant kinematic features over the total kinematic pattern in order to detect intention from observation of simple motor acts. Intention discriminability covaried with movement kinematics on a trial-by-trial basis, and was directly related to the expression of discriminative features in the observed movements. These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others' intentional actions.
| S-EPMC5109236 | biostudies-other
Project description:A new commitment scheme based on position-verification and non-local quantum correlations is presented here for the first time in literature. The only credential for unconditional security is the position of committer and non-local correlations generated; neither receiver has any pre-shared data with the committer nor does receiver require trusted and authenticated quantum/classical channels between him and the committer. In the proposed scheme, receiver trusts the commitment only if the scheme itself verifies position of the committer and validates her commitment through non-local quantum correlations in a single round. The position-based commitment scheme bounds committer to reveal valid commitment within allocated time and guarantees that the receiver will not be able to get information about commitment unless committer reveals. The scheme works for the commitment of both bits and qubits and is equally secure against committer/receiver as well as against any third party who may have interests in destroying the commitment. Our proposed scheme is unconditionally secure in general and evades Mayers and Lo-Chau attacks in particular.
| S-EPMC4209449 | biostudies-other
Project description:Decoding is the key step during protein synthesis that enables information transfer from RNA to protein, a process critical for the survival of all organisms. We have used large-scale (2.64 x 10(6) atoms) all-atom simulations of the entire ribosome to understand a critical step of decoding. Although the decoding problem has been studied for more than four decades, the rate-limiting step of cognate tRNA selection has only recently been identified. This step, known as accommodation, involves the movement inside the ribosome of the aminoacyl-tRNA from the partially bound "A/T" state to the fully bound "A/A" state. Here, we show that a corridor of 20 universally conserved ribosomal RNA bases interacts with the tRNA during the accommodation movement. Surprisingly, the tRNA is impeded by the A-loop (23S helix 92), instead of enjoying a smooth transition to the A/A state. In particular, universally conserved 23S ribosomal RNA bases U2492, C2556, and C2573 act as a 3D gate, causing the acceptor stem to pause before allowing entrance into the peptidyl transferase center. Our simulations demonstrate that the flexibility of the acceptor stem of the tRNA, in addition to flexibility of the anticodon arm, is essential for tRNA selection. This study serves as a template for simulating conformational changes in large (>10(6) atoms) biological and artificial molecular machines.
| S-EPMC1266076 | biostudies-literature
Project description:During reach planning, fronto-parietal brain areas need to transform sensory information into a motor code. It is debated whether these areas maintain a sensory representation of the visual cue or a motor representation of the upcoming movement goal. Here, we present results from a delayed pro-/anti-reach task which allowed for dissociating the position of the visual cue from the reach goal. In this task, the visual cue was combined with a context rule (pro vs. anti) to infer the movement goal. Different levels of movement goal specification during the delay were obtained by presenting the context rule either before the delay together with the visual cue (specified movement goal) or after the delay (underspecified movement goal). By applying functional magnetic resonance imaging (fMRI) multivoxel pattern analysis (MVPA), we demonstrate movement goal encoding in the left dorsal premotor cortex (PMd) and bilateral superior parietal lobule (SPL) when the reach goal is specified. This suggests that fronto-parietal reach regions (PRRs) maintain a prospective motor code during reach planning. When the reach goal is underspecified, only area PMd but not SPL represents the visual cue position indicating an incomplete state of sensorimotor integration. Moreover, this result suggests a potential role of PMd in movement goal selection.
| S-EPMC5323385 | biostudies-literature
Project description:ObjectiveA major challenge for controlling a prosthetic arm is communication between the device and the user's phantom limb. We show the ability to enhance phantom limb perception and improve movement decoding through targeted transcutaneous electrical nerve stimulation in individuals with an arm amputation.ApproachTranscutaneous nerve stimulation experiments were performed with four participants with arm amputation to map phantom limb perception. We measured myoelectric signals during phantom hand movements before and after participants received sensory stimulation. Using electroencephalogram (EEG) monitoring, we measured the neural activity in sensorimotor regions during phantom movements and stimulation. In one participant, we also tracked sensory mapping over 2 years and movement decoding performance over 1 year.Main resultsResults show improvements in the participants' ability to perceive and move the phantom hand as a result of sensory stimulation, which leads to improved movement decoding. In the extended study with one participant, we found that sensory mapping remains stable over 2 years. Sensory stimulation improves within-day movement decoding while performance remains stable over 1 year. From the EEG, we observed cortical correlates of sensorimotor integration and increased motor-related neural activity as a result of enhanced phantom limb perception.SignificanceThis work demonstrates that phantom limb perception influences prosthesis control and can benefit from targeted nerve stimulation. These findings have implications for improving prosthesis usability and function due to a heightened sense of the phantom hand.
| S-EPMC8437134 | biostudies-literature
Project description:Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.
| S-EPMC3986521 | biostudies-other
Project description:New technologies are key to understanding the dynamic activity of neural circuits and systems in the brain. Here, we show that a minimally invasive approach based on ultrasound can be used to detect the neural correlates of movement planning, including directions and effectors. While non-human primates (NHPs) performed memory-guided movements, we used functional ultrasound (fUS) neuroimaging to record changes in cerebral blood volume with 100 μm resolution. We recorded from outside the dura above the posterior parietal cortex, a brain area important for spatial perception, multisensory integration, and movement planning. We then used fUS signals from the delay period before movement to decode the animals' intended direction and effector. Single-trial decoding is a prerequisite to brain-machine interfaces, a key application that could benefit from this technology. These results are a critical step in the development of neuro-recording and brain interface tools that are less invasive, high resolution, and scalable.
| S-EPMC8105283 | biostudies-literature
Project description:Brain-machine interfaces (BMIs) are a rapidly progressing technology with the potential to restore function to victims of severe paralysis via neural control of robotic systems. Great strides have been made in directly mapping a user's cortical activity to control of the individual degrees of freedom of robotic end-effectors. While BMIs have yet to achieve the level of reliability desired for widespread clinical use, environmental sensors (e.g. RGB-D cameras for object detection) and prior knowledge of common movement trajectories hold great potential for improving system performance. Here we present a novel sensor fusion paradigm for BMIs that capitalizes on information able to be extracted from the environment to greatly improve the performance of control. This was accomplished by using dynamic movement primitives to model the 3D endpoint trajectories of manipulating various objects. We then used a switching unscented Kalman filter to continuously arbitrate between the 3D endpoint kinematics predicted by the dynamic movement primitives and control derived from neural signals. We experimentally validated our system by decoding 3D endpoint trajectories executed by a non-human primate manipulating four different objects at various locations. Performance using our system showed a dramatic improvement over using neural signals alone, with median distance between actual and decoded trajectories decreasing from 31.1 cm to 9.9 cm, and mean correlation increasing from 0.80 to 0.98. Our results indicate that our sensor fusion framework can dramatically increase the fidelity of neural prosthetic trajectory decoding.
| S-EPMC5473343 | biostudies-literature
Project description:Magnetoencephalography (MEG) enables a noninvasive interface with the brain that is potentially capable of providing movement-related information similar to that obtained using more invasive neural recording techniques. Previous studies have shown that movement direction can be decoded from multichannel MEG signals recorded in humans performing wrist movements. We studied whether this information can be extracted without overt movement of the subject, because the targeted users of brain-controlled interface (BCI) technology are those with severe motor disabilities. The objectives of this study were twofold: 1) to decode intended movement direction from MEG signals recorded during the planning period before movement onset and during imagined movement and 2) to localize cortical sources modulated by intended movement direction. Ten able-bodied subjects performed both overt and imagined wrist movement while their cortical activities were recorded using a whole head MEG system. The intended movement direction was decoded using linear discriminant analysis and a Bayesian classifier. Minimum current estimation (MCE) in combination with a bootstrapping procedure enabled source-space statistical analysis, which showed that the contralateral motor cortical area was significantly modulated by intended movement direction, and this modulation was the strongest ?100 ms before the onset of overt movement. These results suggest that it is possible to study cortical representation of specific movement information using MEG, and such studies may aid in presurgical localization of optimal sites for implanting electrodes for BCI systems.
| S-EPMC2997025 | biostudies-literature