Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.

Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, offer a direct communication channel between humans and robotic arms. However, the ambiguous and unstable characteristics of EEG signals, coupled with their widespread distribution, make it challenging to collect sufficient data and hinder the calibration performance for new signals, thereby reducing the reliability of EEG-based applications. To address these issues, this study proposes an iteratively calibratable network aimed at enhancing the reliability and efficiency of EEG-based robotic arm control systems. The proposed method integrates feature inputs with network expansion techniques. This integration allows a network trained on an extensive initial dataset to adapt effectively to new users during calibration. Additionally, our approach combines motor imagery and speech imagery datasets to increase not only its intuitiveness but also the number of command classes. The evaluation is conducted in a pseudo-online manner, with a robotic arm operating in real-time to collect data, which is then analyzed offline. The evaluation results demonstrated that the proposed method outperformed the comparison group in 10 sessions and demonstrated competitive results when the two paradigms were combined. Therefore, it was confirmed that the network can be calibrated and personalized using only the new data from new users.

Self-Assembly for Creating Vertically-Aligned Graphene Micro Helices with Monolayer Graphene as Chiral Metamaterials.

Graphene's emergence enables creating chiral metamaterials in helical shapes for terahertz (THz) applications, overcoming material limitations. However, practical implementation remains theoretical due to fabrication challenges. This paper introduces a dual-component self-assembly technique that enables creating vertically-aligned continuous monolayer graphene helices at microscale with great flexibility and high controllability. This assembly process not only facilitates the creation of 3D microstructures, but also positions the 3D structures from a horizontal to a vertical orientation, achieving an aspect ratio (height/width) of ≈2700. As a result, an array of vertically-aligned graphene helices is formed, reaching up to 4 mm in height, which is equivalent to 4 million times the height of monolayer graphene. The benefit of these 3D chiral structures made from graphene is their capability to infinitely extend in height, interacting with light in ways that are not possible with traditional 2D layering methods. Such an impressive height elevates a level of interaction with light that far surpasses what is achievable with traditional 2D layering methods, resulting in a notable enhancement of optical chirality properties. This approach is applicable to various 2D materials, promising advancements in innovative research and diverse applications across fields.

Real-Time Deep Neurolinguistic Learning Enhances Noninvasive Neural Language Decoding for Brain-Machine Interaction.

Electroencephalogram (EEG)-based brain-machine interface (BMI) has been utilized to help patients regain motor function and has recently been validated for its use in healthy people because of its ability to directly decipher human intentions. In particular, neurolinguistic research using EEGs has been investigated as an intuitive and naturalistic communication tool between humans and machines. In this study, the human mind directly decoded the neural languages based on speech imagery using the proposed deep neurolinguistic learning. Through real-time experiments, we evaluated whether BMI-based cooperative tasks between multiple users could be accomplished using a variety of neural languages. We successfully demonstrated a BMI system that allows a variety of scenarios, such as essential activity, collaborative play, and emotional interaction. This outcome presents a novel BMI frontier that can interact at the level of human-like intelligence in real time and extends the boundaries of the communication paradigm.

2020 International brain-computer interface competition: A review.

The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.

Realization of Curved Circular Nanotubes Using In Situ Monitored Self-Assembly.

Curved fluidic channels with a circular cross-section play an important role in biology, chemistry, and medicine. However, in nanofluidics, a problem that is largely unsolved is the lack of an effective fabrication method for curved circular nanotubes (10-1000 nm). In this work, an electron-beam-induced self-assembly process was applied to achieve fine curved nanostructures for the realization of nanofluidic devices. The diameter of the tube could be precisely controlled by an atomic layer deposition process. Fluid transported through the nanochannels was verified and characterized using a dark-field microscope under an optical diffraction limit size. The fluid flow demonstrates that the liquid's evaporation (vapor diffusion) in the nanochannel generates compressed vapor, which pumps the liquid and pushes it forward, resulting in a directional flow behavior in the ∼100 nm radius of tubes. This phenomenon could provide a useful platform for the development of diverse nanofluidic devices.

NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.

Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI related to natural hand-grasp tasks is challenging due to its high complexity. Although numerous BCI studies have successfully decoded large body parts, such as the movement intention of both hands, arms, or legs, research on MI decoding of high-level behaviors such as hand grasping is essential to further expand the versatility of MI-based BCIs. In this study, we propose NeuroGrasp, a dual-stage deep learning framework that decodes multiple hand grasping from EEG signals under the MI paradigm. The proposed method effectively uses an EEG and electromyography (EMG)-based learning, such that EEG-based inference at test phase becomes possible. The EMG guidance during model training allows BCIs to predict hand grasp types from EEG signals accurately. Consequently, NeuroGrasp improved classification performance offline, and demonstrated a stable classification performance online. Across 12 subjects, we obtained an average offline classification accuracy of 0.68 (±0.09) in four-grasp-type classifications and 0.86 (±0.04) in two-grasp category classifications. In addition, we obtained an average online classification accuracy of 0.65 (±0.09) and 0.79 (±0.09) across six high-performance subjects. Because the proposed method has demonstrated a stable classification performance when evaluated either online or offline, in the future, we expect that the proposed method could contribute to different BCI applications, including robotic hands or neuroprosthetics for handling everyday objects.

Refinement of recto-sigmoid colon vaginoplasty using a three-dimensional laparoscopic technique.

ABSTRACT: To investigate the feasibility, safety, and outcomes of three-dimensional (3D) laparoscopic vaginoplasty with a rectosigmoid colon flap for vaginal reconstruction.Following appropriate preoperative patient counseling, 17 consecutive patients underwent vaginoplasty using a 3D laparoscopic system. Perioperative and postoperative outcomes were retrospectively evaluated.Between September 2016 and February 2020, 17 patients underwent 3D laparoscopic vaginoplasty with a rectosigmoid colon flap. Of them, 15 (88%) were transgender female patients, and 2 (12%) were cisgender female patients with congenital deformities. Among the 15 transgender patients, 12 (80%) underwent de novo surgeries and 3 (20%) underwent re-do surgeries. The mean age at the time of operation was 33.0 years, and the mean total operation time was 529 ±â€Š128 minutes. The initial intraoperative mean vaginal depth was 15.2 ±â€Š1.3 cm, and the 30-day readmission rate was 5.9% (1/17 cases). The mean follow-up duration was 24.8 months.Perioperative and postoperative outcomes suggest that 3D laparoscopic rectosigmoid colon vaginoplasty is a potentially acceptable, effective, and safe method for vaginal reconstruction.

Hybridized Radial and Edge Coupled 3D Plasmon Modes in Self-Assembled Graphene Nanocylinders.

Current graphene-based plasmonic devices are restricted to 2D patterns defined on planar substrates; thus, they suffer from spatially limited 2D plasmon fields. Here, 3D graphene forming freestanding nanocylinders realized by a plasma-triggered self-assembly process are introduced. The graphene-based nanocylinders induce hybridized edge (in-plane) and radial (out-of-plane) coupled 3D plasmon modes stemming from their curvature, resulting in a four orders of magnitude stronger field at the openings of the cylinders than in rectangular 2D graphene ribbons. For the characterization of the 3D plasmon modes, synchrotron nanospectroscopy measurements are performed, which provides the evidence of preservation of the hybridized 3D graphene plasmons in the high precision curved nanocylinders. The distinct 3D modes introduced in this paper, provide an insight into geometry-dependent 3D coupled plasmon modes and their ability to achieve non-surface-limited (volumetric) field enhancements.

Electron Beam Maneuvering of a Single Polymer Layer for Reversible 3D Self-Assembly.

Reversible self-assembly that allows materials to switch between structural configurations has triggered innovation in various applications, especially for reconfigurable devices and robotics. However, reversible motion with nanoscale controllability remains challenging. This paper introduces a reversible self-assembly using stress generated by electron irradiation triggered degradation (shrinkage) of a single polymer layer. The peak position of the absorbed energy along the depth of a polymer layer can be modified by tuning the electron energy; the peak absorption location controls the position of the shrinkage generating stress along the depth of the polymer layer. The stress gradient can shift between the top and bottom surface of the polymer by repeatedly tuning the irradiation location at the nanoscale and the electron beam voltage, resulting in reversible motion. This reversible self-assembly process paves the path for the innovation of small-scale machines and reconfigurable functional devices.

Development of a Human-Display Interface with Vibrotactile Feedback for Real-World Assistive Applications.

It is important to operate devices with control panels and touch screens assisted by haptic feedback in mobile environments such as driving automobiles and electric power wheelchairs. A lot of consideration is needed to give accurate haptic feedback, especially, presenting clear touch feedback to the elderly and people with reduced sensation is a very critical issue from healthcare and safety perspectives. In this study, we aimed to identify the perceptual characteristics for the frequency and direction of haptic vibration on the touch screen with vehicle-driving vibration and to propose an efficient haptic system based on these characteristics. As a result, we demonstrated that the detection threshold shift decreased at frequencies above 210 Hz due to the contact pressure during active touch, but the detection threshold shift increased at below 210 Hz. We found that the detection thresholds were 0.30-0.45 gpeak with similar sensitivity in the 80-270 Hz range. The haptic system implemented by reflecting the experimental results achieved characteristics suitable for use scenarios in automobiles. Ultimately, it could provide practical guidelines for the development of touch screens to give accurate touch feedback in the real-world environment.

Radiation-induced IL-1ß expression and secretion promote cancer cell migration/invasion via activation of the NF-κB-RIP1 pathway.

Here, we demonstrate that interleukin-1ß (IL-1ß) contributes to the γ-ionizing radiation (IR)-induced increase of migration/invasion in A549 lung cancer cells, and that this occurs via RIP1 upregulation. We initially observed that the protein expression and secreted concentration of IL-1ß were increased upon exposure of A549 cells to IR. We then demonstrated that IR-induced IL-1ß is located downstream of the NF-κB-RIP1 signaling pathway. Treatments with siRNA and specific pharmaceutical inhibitors of RIP1 and NF-κB suppressed the IR-induced increases in the protein expression and secreted concentration of IL-1ß. IL-1Ra, an antagonist of IL-1ß, treatment suppressed the IR-induced epithelial-mesenchymal transition (EMT) and IR-induced invasion/migration in vitro. These results suggest that IL-1ß could regulate IR-induced EMT. We also found that IR could induce the expression of IL-1ß expression in vivo and that of IL-1 receptor (R) I/II in vitro and in vivo. The IR-induced increases in the protein levels of IL-1 RI/II and IL-1ß suggest that an autocrine loop between IL-1ß and IL-1 RI/II might play important roles in IR-induced EMT and migration/invasion. Based on these collective results, we propose that IR concomitantly activates NF-κB and RIP1 to trigger the NF-κB-RIP1-IL-1ß-IL-1RI/II-EMT pathway, ultimately promoting metastasis.

Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions.

BACKGROUND: Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding, which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants. FINDINGS: We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. CONCLUSIONS: The dataset includes the data of multiple recording sessions, various classes within the single upper extremity, and multimodal signals. This work can be used to (i) compare the brain activities associated with real movement and imagination, (ii) improve the decoding performance, and (iii) analyze the differences among recording sessions. Hence, this study, as a Data Note, has focused on collecting data required for further advances in the BCI technology.

Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy.

Electroencephalogram (EEG) based braincomputer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49(±8.74)% for motor execution (ME) and 40.36(±3.39)% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.

Self-Assembled 3D Nanosplit Rings for Plasmon-Enhanced Optofluidic Sensing.

Plasmonic sensors are commonly defined on two-dimensional (2D) surfaces with an enhanced electromagnetic field only near the surface, which requires precise positioning of the targeted molecules within hotspots. To address this challenge, we realize segmented nanocylinders that incorporate plasmonic (1-50 nm) gaps within three-dimensional (3D) nanostructures (nanocylinders) using electron irradiation triggered self-assembly. The 3D structures allow desired plasmonic patterns on their inner cylindrical walls forming the nanofluidic channels. The nanocylinders bridge nanoplasmonics and nanofluidics by achieving electromagnetic field enhancement and fluid confinement simultaneously. This hybrid system enables rapid diffusion of targeted species to the larger spatial hotspots in the 3D plasmonic structures, leading to enhanced interactions that contribute to a higher sensitivity. This concept has been demonstrated by characterizing an optical response of the 3D plasmonic nanostructures using surface-enhanced Raman spectroscopy (SERS), which shows enhancement over a 22 times higher intensity for hemoglobin fingerprints with nanocylinders compared to 2D nanostructures.

RIP1 Is a Novel Component of γ-ionizing Radiation-Induced Invasion of Non-Small Cell Lung Cancer Cells.

Abstract: Previously, we demonstrated that γ-ionizing radiation (IR) triggers the invasion/migration of A549 cells via activation of an EGFR-p38/ERK-STAT3/CREB-1-EMT pathway. Here, we have demonstrated the involvement of a novel intracellular signaling mechanism in γ-ionizing radiation (IR)-induced migration/invasion. Expression of receptor-interacting protein (RIP) 1 was initially increased upon exposure of A549, a non-small cell lung cancer (NSCLC) cell line, to IR. IR-induced RIP1 is located downstream of EGFR and involved in the expression/activity of matrix metalloproteases (MMP-2 and MMP-9) and vimentin, suggesting a role in epithelial-mesenchymal transition (EMT). Our experiments showed that IR-induced RIP1 sequentially induces Src-STAT3-EMT to promote invasion/migration. Inhibition of RIP1 kinase activity and expression blocked induction of EMT by IR and suppressed the levels and activities of MMP-2, MMP-9 and vimentin. IR-induced RIP1 activation was additionally associated with stimulation of the transcriptional factor NF-κB. Specifically, exposure to IR triggered NF-κB activation and inhibition of NF-κB suppressed IR-induced RIP1 expression, followed by a decrease in invasion/migration as well as EMT. Based on the collective results, we propose that IR concomitantly activates EGFR and NF-κB and subsequently triggers the RIP1-Src/STAT3-EMT pathway, ultimately promoting metastasis.

Electron Irradiation Driven Nanohands for Sequential Origami.

Sequence plays an important role in self-assembly of 3D complex structures, particularly for those with overlap, intersection, and asymmetry. However, it remains challenging to program the sequence of self-assembly, resulting in geometric and topological constrains. In this work, a nanoscale, programmable, self-assembly technique is reported, which uses electron irradiation as "hands" to manipulate the motion of nanostructures with the desired order. By assigning each single assembly step in a particular order, localized motion can be selectively triggered with perfect timing, making a component accurately integrate into the complex 3D structure without disturbing other parts of the assembly process. The features of localized motion, real-time monitoring, and surface patterning open the possibility for the further innovation of nanomachines, nanoscale test platforms, and advanced optical devices.

ß-Apopicropodophyllin functions as a radiosensitizer targeting ER stress in non-small cell lung cancer.

AIMS: In this study, we examined whether ß-apopicropodophyllin (APP) could act as a radiosensitizer in non-small cell lung cancer (NSCLC) cells. MAIN METHODS: The in vitro radiosensitizing activity of APP was demonstrated with clonogenic assay, immunoblotting, Annexin V-Propidium iodide (PI) assay, BrdU incorporation, detection of mitochondrial ROS/intracellular of H2O2, mitochondrial membrane potential detection, and performing of isolation of mitochondrial and cytosolic fractions. The in vivo radiosensitizing activity of APP was determined in xenografted mice with co-treatment of APP and IR based on measurement of tumor volumes and apoptotic cell death. KEY FINDINGS: The results of a clonogenic assay indicated that a combination of APP and γ-ionizing radiation (IR) inhibits cell growth and increases cell death in NSCLC cells. Several signal transduction pathways were examined for their potential involvement in the apparent radiosensitization effect of APP, as assessed by immunoblotting analyses and mitochondrial potential determination in vitro. Treatment of NCI-H460 cells with 15 nM APP and NCI-H1299 cells with 10 nM APP yielded dose-enhancement ratios of 1.44 and 1.24, respectively. Enhanced ER stress, disrupted mitochondrial membrane potential, and increased reactive oxygen species (ROS) were observed in cells co-treated with APP and IR, and this was followed by the cytosolic release of cytochrome c and consequent activation of caspase-3 and -9. Notably, inhibition of JNK, which prevents caspase activation, blocked the APP/IR-induced activations of ER stress and apoptotic cell death. In NCI-H460 or NCI-H1299 cell-xenografted mice, APP/IR treatment delayed the time it took tumors to reach a threshold size by 22.38 and 16.83 days, respectively, compared with controls, to yield enhancement factors of 1.53 and 1.38, respectively. SIGNIFICANCE: APP has a radiosensitizing function derived from its ability to induce apoptotic cell death via activation of ER stress, disruption of mitochondrial membrane potential, and induction of the caspase pathway.

Reconstructing Degree of Forearm Rotation from Imagined movements for BCI-based Robot Hand Control.

Brain-computer interface (BCI) is an important tool for rehabilitation and control of an external device (e.g., robot arm or home appliances). Fully reconstruction of upper limb movement from brain signals is one of the critical issues for intuitive BCI. However, decoding of forearm rotation from imagined movements using electroencephalography (EEG) is difficult to decode degree of rotation accurately. In this paper, we reconstructed imagined forearm rotation from low- frequency (0.3-3 Hz) of EEG signals. We selected 20 EEG channel on motor cortex for analysis. Ten healthy subjects participated in our experiment. The subjects performed actual and imagined forearm rotation to reach different targets. We trained a reconstruction decoder which used the EEG signals measured from actual movements and the kinematic information only. Additionally, we applied a long short-term memory (LSTM) network to enhance decoding performances. As a result, we achieved the high correlation performance (Average: 0.67) to decode imagined forearm rotation angle. This result has demonstrated that the reconstruction decoder which is trained by the EEG data from actual movement has effective to decode robustly for the imagined forearm rotation angle.

Nano-Architecture Driven Plasmonic Field Enhancement in 3D Graphene Structures.

The limited spatial coverage of the plasmon enhanced near-field in 2D graphene ribbons presents a major hurdle in practical applications. In this study, diverse self-assembled 3D graphene architectures are explored that induce hybridized plasmon modes by simultaneous in-plane and out-of-plane coupling to overcome the limited coverage in 2D ribbons. While 2D graphene can only demonstrate in-plane, bidirectional coupling through the edges, 3D architectures benefit from fully symmetric 360° coupling at the apex of pyramidal graphene, orthogonal four-directional coupling in cubic graphene, and uniform cross-sectional radial coupling in tubular graphene. The 3D coupled vertices, edges, surfaces, and volume induce corresponding enhancement modes that are highly dependent on the shape and dimensions comprising the 3D geometries. The hybridized modes introduced through the 3D coupling amplify the limited plasmon response in 2D ribbons to deliver nondiffusion limited sensors, high efficiency fuel cells, and extreme propagation length optical interconnects.

Fabrication of Three-Dimensional Graphene-Based Polyhedrons via Origami-Like Self-Folding.

The assembly of two-dimensional (2D) graphene into three-dimensional (3D) polyhedral structures while preserving the graphene's excellent inherent properties has been of great interest for the development of novel device applications. Here, fabrication of 3D, microscale, hollow polyhedrons (cubes) consisting of a few layers of 2D graphene or graphene oxide sheets via an origami-like self-folding process is described. This method involves the use of polymer frames and hinges, and aluminum oxide/chromium protection layers that reduce tensile, spatial, and surface tension stresses on the graphene-based membranes when the 2D nets are transformed into 3D cubes. The process offers control of the size and shape of the structures as well as parallel production. In addition, this approach allows the creation of surface modifications by metal patterning on each face of the 3D cubes. Raman spectroscopy studies show the method allows the preservation of the intrinsic properties of the graphene-based membranes, demonstrating the robustness of our method.