Robot-assisted therapy has long promised to advance stroke rehabilitation by delivering intensive and personalized training, yet its clinical impact remains limited. Closing the sensorimotor loop with brain-computer interfaces offers a better strategy than passive mobilization, directly linking user intent to robotic assistance and potentially driving neuroplasticity. However, a brain-computer interface requires subject-specific calibration that is time-consuming and often impractical. Moreover, brain decoding remains error-prone due to variability of neural signals, thus resulting in unintended robot actions that could reduce engagement during closed-loop control. Here, we demonstrate that a decoder trained on an expert subject can be transferred to na & iuml;ve users for online control of a rehabilitation exoskeleton in a rest-versus-reaching paradigm, a functional task with clinical relevance. We then characterize error-related potentials arising from expectation mismatches between brain commands and robot actions during closed-loop control. Finally, we show that these mismatches can be reliably decoded in a subject-independent framework (mean area under the receiver operating characteristic curve: 0.77), a crucial step toward rehabilitation scenarios where collecting subject-specific error-related potential data is challenging. Our findings highlight the potential for integrating real-time error-detection to enhance human-robot interaction by correcting unintended robot behaviors, which could significantly improve rehabilitation outcomes where accurate and contingent feedback is essential.

Maladaptive plasticity is a counterproductive neural reorganization in response to injury that hinders functional recovery. This may be corrected by inducing further plasticity in nodes of the dysfunctional network. Repetitive transcranial magnetic stimulation (rTMS) can focally alter cortical excitability. However, its effect on functional activation patterns is understudied, especially when activation patterns, such as the maladaptive ones, are reinforced. As a model case in healthy subjects, we investigate the effect of low-frequency rTMS on individualized, multivariate patterns of motor cortex activation decoded and reinforced by a brain-computer interface (BCI) with feedback. Low-frequency rTMS, delivered in open-loop, shows a trend towards inhibiting motor activation. But its effect is abrogated with continuous motor imagery BCI feedback. We developed a novel closed-loop BCI-triggered rTMS system that delivers low-frequency stimulation when maladaptive patterns are decoded. We demonstrate that closed-loop stimulation can inhibit motor activation during continuous motor imagery BCI control. Our results support the use of rTMS, open and closed-loop, for correcting maladaptive plastic changes underlying central nervous system disorders.

Robot-assisted therapy can deliver high-dose, task-specific training after neurologic injury, but most systems act primarily at the limb level - engaging the impaired neural circuits only indirectly - which remains a key barrier to truly contingent, neuroplasticity-targeted rehabilitation. We address this gap by implementing online, dual-state motor imagery control of an upper-limb exoskeleton, enabling goal-directed reaches to be both initiated and terminated directly from noninvasive EEG. Eight participants used EEG to initiate assistance and then volitionally halt the robot mid-trajectory. Across two online sessions, group-mean hit rates were 61.5% for onset and 64.5% for offset, demonstrating reliable start–stop command delivery despite instrumental noise and passive arm motion. Methodologically, we reveal a systematic, class-driven bias induced by common task-based recentering using an asymmetric margin diagnostic, and we introduce a class-agnostic fixation-based recentering method that tracks drift without sampling command classes while preserving class geometry. This substantially improves threshold-free separability (AUC gains: onset +56%, p=0.0117; offset +34%, p=0.0251) and reduces bias within and across days. Together, these results help bridge offline decoding and practical, intention-driven start–stop control of a rehabilitation exoskeleton, enabling precisely timed, contingent assistance aligned with neuroplasticity goals while supporting future clinical translation.

Objective.Non-invasive electroencephalograms (EEG)-based brain-computer interfaces (BCIs) play a crucial role in a diverse range of applications, including motor rehabilitation, assistive and communication technologies, holding potential promise to benefit users across various clinical spectrums. Effective integration of these applications into daily life requires systems that provide stable and reliable BCI control for extended periods. Our prior research introduced the AIRTrode, a self-adhesive (A), injectable (I), and room-temperature (RT) spontaneously-crosslinked hydrogel electrode (AIRTrode). The AIRTrode has shown lower skin-contact impedance and greater stability than dry electrodes and, unlike wet gel electrodes, does not dry out after just a few hours, enhancing its suitability for long-term application. This study aims to demonstrate the efficacy of AIRTrodes in facilitating reliable, stable and long-term online EEG-based BCI operations.Approach.In this study, four healthy participants utilized AIRTrodes in two BCI control tasks-continuous and discrete-across two sessions separated by six hours. Throughout this duration, the AIRTrodes remained attached to the participants' heads. In the continuous task, participants controlled the BCI through decoding of upper-limb motor imagery (MI). In the discrete task, the control was based on decoding of error-related potentials (ErrPs).Main Results.Using AIRTrodes, participants demonstrated consistently reliable online BCI performance across both sessions and tasks. The physiological signals captured during MI and ErrPs tasks were valid and remained stable over sessions. Lastly, both the BCI performances and physiological signals captured were comparable with those from freshly applied, research-grade wet gel electrodes, the latter requiring inconvenient re-application at the start of the second session.Significance.AIRTrodes show great potential promise for integrating non-invasive BCIs into everyday settings due to their ability to support consistent BCI performances over extended periods. This technology could significantly enhance the usability of BCIs in real-world applications, facilitating continuous, all-day functionality that was previously challenging with existing electrode technologies.

OBJECTIVE:A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS). APPROACH:Previous studies have identified that the peak power spectral density (PSD) value in sensorimotor idling rhythms is a neural correlate of participants' upper limb MI-BCI performances. In this active-controlled, single-blind study, we applied 20 minutes of tACS at the participant-specific, peak µ frequency in resting-state sensorimotor rhythms (SMRs), with the goal of enhancing resting-state µ SMRs. MAIN RESULTS:After tACS, we observed significant improvements in event-related desynchronizations (ERDs) of µ sensorimotor rhythms (SMRs), and in the performance of an online MI-BCI that decodes left versus right hand commands in healthy participants (N=10) -but not in an active control-stimulation control group (N=10). Lastly, we showed a significant correlation between the resting-state µ SMRs and µ ERD, offering a mechanistic interpretation behind the observed changes in online BCI performances. SIGNIFICANCE:Our research lays the groundwork for future non-invasive interventions designed to enhance BCI performances, thereby improving the independence and interactions of individuals who rely on these systems.

Non-invasive brain-computer interfaces (BCIs) hold promise for restoring motor function, yet they are typically confined to seated laboratory settings and require calibration-a crucial open-loop step for building decoders used in closed-loop control. Extending BCIs to real-world scenarios such as walking, a fundamental activity of daily living, is challenging due to non-stationarities of neural signals even in seated conditions. During walking, decoding is further exacerbated by movement-related artifacts, which hinder calibration and degrade decoder quality, impairing online performance. While motor imagery (MI) is commonly used for real-time BCI control, its application during walking remains underexplored. We conducted a longitudinal study with nine participants, who first completed online MI-BCI sessions while seated, based on calibration data collected while sitting. The trained decoder was then transferred to walking sessions using domain adaptation and noise reconstruction to address non-stationarities and movement artifacts. Seated-trained decoders were successfully applied during walking, with only a moderate performance reduction. Decoding accuracy remained strongly correlated across conditions ($r=0.6391$), suggesting consistent MI patterns. Ablation experiments highlighted the critical role of domain adaptation in enabling robust decoder transfer. These findings support the feasibility of calibration-efficient, real-world BCI applications in dynamic and mobile environments.

The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.

Electroencephalography (EEG) is a cornerstone in both neuroscience research and clinical diagnostics. However, conventional EEG monitoring faces hardware limitations, particularly its adaptability and stability. Headsets either require complicated wiring or do not have enough stretchability and wearability to comply with the diverse head anthropometry and hair conditions of the user population. Additionally, there is an inherent tradeoff between wet and dry electrodes in capturing high-fidelity signals from hair-covered scalp regions while ensuring continuous and long-term recording quality. Here, we present a Mesh-integrated, Stretchable, and Hair-compatible EEG system engineered to overcome these limitations. By incorporating a kirigami-inspired mesh design and stretchable eutectic Gallium-Indium interconnects, MindStretcH adapts to various head sizes and allows for easy wearing and removal. Moreover, its uniquely designed porous, conical, soft 3D-printed mold, embedded with hydrogel electrodes, effectively penetrates hair layers to deliver low impedance and sustained signal integrity with minimal discomfort. We validate MindStretcH through offline and online EEG-based brain-computer interface tasks over a month, demonstrating its exceptional stability in continuous monitoring and dynamic applications. These results mark a promising advance toward non-invasive neural interfaces in both clinical and everyday use. ### Competing Interest Statement The authors declare the following competing financial interest(s): A patent application relating to this work has been filed. Alzheimer's Association, https://ror.org/0375f4d26, Department of Defense (DoD) Defense Advanced Research Projects Agency (DARPA),

The Reaction Diffusion Drift (RDD) model is incorporated in the Sentaurus Technology CAD (TCAD) framework and coupled with carrier and lattice heating to calculate the generation of traps during channel hot carrier stress. The parametric shift due to these generated defects, localized near the drain junction of the device, is calculated under different combinations of gate and drain bias stress. The developed TCAD framework is validated against measured data for devices having varying gate length, oxide thickness, and junction structure. The ability of the framework to reproduce measured time kinetics, gate and drain bias, and temperature dependence is demonstrated. The absence of recovery after hot carrier stress is explained using the stochastic implementation of the same model. A 1-D standalone version of the same model, having identical time kinetics as TCAD, together with the Bias Temperature Instability (BTI) analysis tool discussed in Part I, is used to isolate the BTI and pure Hot Carrier Degradation (HCD) contribution during HCD stress. An equivalent compact model is used for cycle-by-cycle simulation of circuit aging due to HCD in different Ring Oscillator (RO) stages, by using the framework discussed in Part I. The error associated with blanket assignment of AC-to-DC ratio is demonstrated.

The Reaction-Diffusion-Drift model is validated as a trap generation framework during Bias Temperature Instability (BTI), Stress Induced Leakage Current (SILC), and Time Dependent Dielectric Breakdown (TDDB) experiments. The model is implemented in standalone and Technology CAD (TCAD)-based deterministic and standalone stochastic versions. Different implementations show equivalence of the time kinetics of trap generation during stress and trap passivation after stress. The trigger for different type of experiments is introduced via a single reaction parameter. The model is validated against measured data under diverse experimental conditions, either solely, or along with other models to account for additional physical processes. A circuit simulation platform that uses the physical trap generation model is utilized to estimate activity aware aging in logic circuits due to BTI. The error associated with effective AC duty simulation is shown. Implementation and validation for the Hot Carrier Degradation (HCD) is presented in part-II of this article.

Plantlet is a recent lightweight stream cipher designed by Mikhalev, Armknecht and Müller in IACR ToSC 2017. This design paradigm receives attention as it is secure against generic time–memory–data trade-off attacks despite its small internal state size. One major motivation for Plantlet is to shore up the weaknesses of Sprout, which is another lightweight stream cipher from the same designers in IACR FSE 2015. In this paper, we observe that a full key recovery attack is possible using a restricted version of near collision attack. We have listed 38 internal state differences whose keystream differences have some fixed 0/1 pattern at certain positions and are efficient for our attack. An adversary in the online phase looks for any one of those 38 patterns in keystream difference. If found then with some probability, the adversary guesses the internal state difference. Afterwards, on solving a system of polynomial equations (formed by keystream bits) using a SAT solver, the adversary can recover the secret key if the guess is correct; otherwise, some contradiction occurs. After probability computations, we find that on repeating the experiment for a fixed number of times, the adversary can recover the secret key with expectation one. The time complexity of the whole process is 2^64.693 Plantlet encryptions which is 39 times faster than the previous best key recovery attack by Banik et al. in IACR ToSC 2019. We further suggest a countermeasure and its analysis to avoid our attacks. However, the complexity presented in this paper is dependent on the system architecture and implementation of the cipher.

Conditional Time-Memory-Data Trade-off (TMDTO) attack given by Biryukov and Shamir can be reduced to the following problem: “Find the minimum number of state bits that should be fixed in order to recover the maximum number of state bits by utilizing the keystream bits and value of rest of the state bits”. As per our literature survey, existing algorithms search for state bits that should be fixed (as minimum as possible) in order to recover the maximum possible state bits directly through the keystream bits. However, those algorithms are cipher specific and require extensive manual effort in analyzing the keystream bit equations. In this manuscript, we have constructed an automated framework that is easy to implement and solves the above problem (for the case when bits are fixed to 0) for any NLFSR based stream cipher with better complexity, thereby reducing manual efforts. However, we do not claim any global optimum for fixed bits. We tried to reduce the number of fixed bits as much as possible. To show that our algorithm is applicable to a majority of NLFSR based stream ciphers, we implement it on three different stream ciphers: LIZARD, GRAIN-128a and ESPRESSO. It improves all existing TMDTO results on these ciphers. The framework involves modelling keystream bit equations into a set of linear constraints, which is then solved by using a Mixed Integer Linear Programming (MILP) solver, Gurobi. The advantages of our automated framework over other methods are that we can achieve better results with far less effort, and it can be applied to any stream cipher of a similar structure with very ease. To the best of our knowledge, our MILP model is the first work that converts the conditional TMDTO of a stream cipher into a linear optimization problem. As a consequence, for LIZARD cipher, we reduce the number of fixed bits by 20 bits from the previous best result when the number of recovered bits is 18. In the case of GRAIN-128a, the highest reduction in the number of fixed bits is by 34 bits when the number of recovered bits is 35. Lastly, for ESPRESSO cipher, the reduction is by 7 bits when the number of recovered bits is 35.

Side Channel Analysis (SCA) is among the newly emerged threats to small scale devices performing a cryptographic operation. While such analysis is well studied against the block ciphers, we observe that the stream cipher counterpart is not that much explored. We propose novel modelling that can work with a number of stream ciphers and related constructions. We show practical state/key recovery attacks on the lightweight ciphers, LIZARD, PLANTLET and GRAIN-128-AEAD. We consider the software platform (where the Hamming weight leakage is available) as well as the hardware platform (where the Hamming distance leakage is available). Through the modelling of Satisfiability Modulo Theory (SMT), we show that the solution can be obtained in a matter of seconds in most cases. In a handful of cases, however, the entire state/key recovery is not feasible in a practical amount of time. For those cases, we show full recovery is possible when a small number of bits are guessed. We also study the effect of increasing/decreasing the number of keystream bits on the solution time. Following a number of literature, we initially assume the traces that are obtained are noiseless. Later, we show how an extension of our model can deal with the noisy traces (which is a more general assumption).

The (K0.9Li0.1)[(Ta0.2Nb0.8)(0.99)Mn-0.01]O-3 (KLTN) electroceramics exhibit fast humidity response, long time resistance stability under various humidity conditions, and giant sensitivity (asymptotic to 2.6 x 10(4)) at 95% RH, which is approximately five times higher than pure KTa0.2Nb0.8O3 (KTN) polycrystalline sample. The KLTN shows a small hysteresis in resistance values during adsorption and desorption measurement within the humidity range of 15%-95%. The current-voltage data indicate the presence of an open-circuit voltage (V-OCV) of 1.6 V with short circuit current (I-SCC) 1-2 mu A at 95% relative humidity, which in turn glows a standard red light-emitting diode (LED) after series and parallel connection of a few humidity sensors. This may be useful for energy harvesting in the remote and humid coastal region; however, it requires further optimization for real applications as energy harvesters. After several repeated cycles, the adsorption and desorption data suggest that KLTN may be a potential candidate for sensitive humidity sensors and energy harvesters.

Maintaining Social Distancing among people is very crucial at present when the whole world is facing the COVID-19 pandemic, and yet there is no effective cure or antidote is available. In this pandemic situation, we need a robust solution to flatten the curve of COVID-19 so that people do not get sick, so our economy and supply chain stays running, and people’s risk can be minimized. Some techniques are available to maintain social distancing, but most are based on visual recognition, which is not effectively applicable everywhere and comparatively expensive. To overcome this issue and make it more effective and smoother, an android application has been developed for the Social Distancing Alert System (SDAS). The system is based on Bluetooth Low Energy (BLE) proximity detection technology, which uses the Received Signal Strength Indicator (RSSI) and Transmission Power (TxPower) of user’s android handsets for the estimation of real-time social distancing status and to alert them. It notifies users through real-time popup notifications on the screen of the handsets and by vibration and notification sound when another app user gets closer to a range of 2 meters with four levels of granularity. This application also provides other compelling features like past social distancing status tracking of the past 30 days, power-saving, and QR scanning to ignore specific people without an internet connection.

A stochastic Reaction-Diffusion-Drift (RDD) model framework is proposed for trap time kinetics under Hot Carrier Degradation (HCD) stress and post-stress conditions. Consistency of the 3-D stochastic RDD, 3-D TCAD incorporated deterministic RDD and an “equivalent” 1-D deterministic RDD frameworks is shown. Measured HCD kinetics is decoupled into contributions by pure HCD and Bias Temperature Instability (BTI). The pure HCD time kinetics during and after stress is modeled using the above frameworks. Lack of recovery for the pure HCD component after stress is explained.

MEmory Reliability Investigation Tool (MERIT) framework, with a generic Reaction-Diffusion-Drift (RDD) model is used to simulate the channel interface $(\Delta\mathrm{N}_{\mathrm{I}\mathrm{T}})$ and bulk oxide $(\Delta\mathrm{N}_{\mathrm{O}\mathrm{T}})$ traps time kinetics in the tunnel oxide (TO) of NAND Flash during Erase-Program (EP) cycling and retention bake after cycling. The generation and passivation of traps are calculated from cycle-to-cycle during distributed EP cycling, and trap passivation is calculated during bake. The framework can model multiple EP cycling phases with varying EP cycling delays, and EP cycling and bake temperature (T). The use of different EP cycling T to mimic the distributed cycling impact is analyzed. The Universal Detrapping Metric (UDM) for various inserted delays and cycling temperatures is verified.

RADAR is an electromagnetic sensor used to detect and locate the target. An FPGA based X-band FMCW radar system will be built with digital Doppler processor and used for detection of target, localization, Doppler processing, velocity calculation and Synthetic Aperture Radar (SAR)/Inverse SAR (ISAR) imaging etc. This radar is a ground based stationary ballistic radar, which transmits a sweep of frequency with a variable Pulse Repetition Frequency (PRF). Return signals called as echo from the target is processed to find the information about their location and velocity. Discrete RF components will be used in the transmitter and receiver chains. The hardware solution is based on Xilinx FPGA board which is responsible for the control of radar system and the digital signal processing of the received signal that involves a Constant False Alarm Rate (CFAR).

A deterministic reaction-diffusion–drift model is used for the time kinetics of bulk gate insulator trap generation in p-channel Field Effect Transistors (FETs) under inversion stress. The consistency of the deterministic and stochastic versions of the model is shown. The model is independently validated using stress-induced leakage current data from various reports. The model is incorporated into the already existing bias temperature instability (BTI) analysis tool framework and validated using negative BTI data. The measured data from FinFETs having different channel material, substrate type, gate insulator process, and fin length, as well as gate-all-around stacked nano sheet (GAA-SNS) FETs are modeled.