Our U-Sleep-based REM sleep detector based on only EEG and EOG data showed good performance in detecting REM sleep. However, it performed considerably worse in RBD, especially in PD with RBD. Using transfer learning with fine-tuning by expert review, a high-performance REM sleep-detecting system will be realized.

In this "centenary" paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8-12 Hz) in 1924, his foresight of substantial clinical applications in patients with "senile dementia," and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients' quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.

Our U-Sleep-based REM sleep detector based on only EEG and EOG data showed good performance in detecting REM sleep. However, it performed considerably worse in RBD, especially in PD with RBD. Using transfer learning with fine-tuning by expert review, a high-performance REM sleep-detecting system will be realized.

In this "centenary" paper, an expert panel revisited Hans Berger's groundbreaking discovery of human restingstate electroencephalographic (rsEEG) alpha rhythms (8-12 Hz) in 1924, his foresight of substantial clinical applications in patients with "senile dementia," and new developments in the field, focusing on Alzheimer's disease (AD), the most prevalent cause of dementia in pathological aging. Clinical guidelines issued in 2024 by the US National Institute on Aging-Alzheimer's Association (NIA-AA) and the European Neuroscience Societies did not endorse routine use of rsEEG biomarkers in the clinical workup of older adults with cognitive impairment. Nevertheless, the expert panel highlighted decades of research from independent workgroups and different techniques showing consistent evidence that abnormalities in rsEEG delta, theta, and alpha rhythms (< 30 Hz) observed in AD patients correlate with wellestablished AD biomarkers of neuropathology, neurodegeneration, and cognitive decline. We posit that these abnormalities may reflect alterations in oscillatory synchronization within subcortical and cortical circuits, inducing cortical inhibitory-excitatory imbalance (in some cases leading to epileptiform activity) and vigilance dysfunctions (e.g., mental fatigue and drowsiness), which may impact AD patients' quality of life. Berger's vision of using EEG to understand and manage dementia in pathological aging is still actual.

<i>Objective</i>. Reaching hand movement is an important motor skill actively examined in the brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.<i>Approach</i>. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.<i>Main results</i>. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson's correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively.<i>Significance</i>. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.

A growing body of neuroscience evidence indicates that perceiving everyday objects and environments triggers mental simulations of potential actions, preparing the brain for movement even before any motion occurs. Although previous research on navigation has demonstrated how the spatial configuration of corridors and doorways guide locomotion, it remains unclear whether architectural elements (e.g., doors, windows, stairs) that require direct interaction evoke distinct, action-specific representations in the brain. Thus, the aim of this study is to investigate whether the passive perception of such elements (viewed without any intention to act) spontaneously recruits motor-related brain networks and whether these activations are specific to the actions implied by each element. fMRI data were collected from 31 participants during passive viewing of each architectural element followed by imagery of four actions: one reflecting the element's primary function, one representing manipulation, and two corresponding to peripheral actions. Using multi-voxel pattern analysis, we created classification ratio maps to identify action-architectural element associations. Our analyses revealed that passive perception significantly elicits neural patterns aligned with the elements' primary function while also encoding sensorimotor representations of the required interactions. Furthermore, our results show that this anticipatory process activates motor-related regions, including premotor cortex and the dorsal visual pathway. These results underscore the crucial role of action simulation in architectural perception and demonstrate the brain's readiness to engage with environmental affordances at the sensorimotor level. Consequently, we propose possibilities for a neuroscience-informed design, suggesting that architecture can be optimized to align with the embodied nature of human cognition.

This study explored the essence and meaning of the recovery experiences of female methamphetamine addicts to deepen the understanding of their recovery process. To this end, five female drug addicts who had been abstinent for more than one year but less than five years were recruited through snowball sampling, and analysis was conducted using Giorgi's (1985) phenomenological qualitative research method. As a result, five essential themes and 19 subthemes were derived. This study confirmed the importance of social and spiritual resources in the recovery process and discussed the need to establish an integrated support system to reinforce them. Additionally, it provided a deep understanding of the social difficulties and structural issues experienced by female addicts, and suggested therapeutic implications and recommendations for future research from the perspective of sustainable recovery from drug addiction.

An amputee due to birth trauma represents a very unique case, in which amputation occurred from delivery process itself. Providing appropriate prosthetics is crucial for enabling such case to improve their daily workout and provide entirely new experiences. However, in such case, conventional surface electromyography (sEMG)-based methods for prosthetic control are unsuitable due to the lack of sufficient sEMG signals, as a case has never used the amputated limb since birth. As an alternative to sEMG, we apply an electroencephalography (EEG)-based approach for prosthetic control. In this study, we demonstrated the feasibility of EEG-based motor imagery decoding for hand grasp types to control a prosthetic hand. We collected 10 days of EEG data about one subject and achieved a 10x5-fold cross-validation accuracy of 0.8740 ± 0.0965 in classifying cylindrical and lateral grasps using time delay embedding (TDE) and tangent space mapping (TSM). Our results demonstrate the effectiveness of TSM as an EEG decoder and the performance improvement achieved by TDE. Future work will focus on adapting this approach for real-world brain-computer interface applications.

Connectome network analysis across multiple species should help identify principles of brain function. Here, we examined three fundamental properties-global efficiency, global betweenness centrality, and global clustering-in the mesoscale tract-tracing data of the mouse connectome; and conducted vulnerability analysis to identify the critical regions and connections based on the loss in network function when each brain region (213) and connection (16,594) was removed. Robustness tests examining noise effects were also conducted. There were five key findings. First, we identified eight critical regions and 38 critical connections, with more central, limbic regions dominant; and with robustness analysis showing (a) the importance of connection strength; and (b) the findings being robust to noise. Second, although critical regions and connections were significantly based on their local network properties, global influences sometimes deviated from local ones (e.g., critical globally but with lower local scores), thereby revealing global-level interactions. Third, the critical components organized into two main pathways (one from piriform cortex to globus pallidus; the other, entorhinal cortex to the amygdala), and two main clusters (centred on caudoputamen and entorhinal cortex). Fourth, for brain function, all main categories from perception to action were represented: e.g., olfaction (piriform cortex), learning and memory (entorhinal cortex), affect (amygdala and caudoputamen), and cognitive and motor processing (caudoputamen, globus pallidus). Finally, the claustrum was intriguingly identified as critical, perhaps for information integration and motor translation. Vulnerability analysis provides a unique approach to characterizing the fundamental structure of nervous systems.

This paper presents a switched-capacitor-driven on-chip interface (S-CDI) that enhances signal integrity and maintains a stable channel DC bias at Vdd/2. It employs alternating pre-charging and charge-sharing using dual series capacitors (Cup/Cdn). This approach enables the use of smaller capacitance while maintaining comparable signal swing to that of prior cap-driven interfaces, leading to higher bandwidth and improved eye-opening margin. Fabricated in a 28 nm CMOS process, the S-CDI achieves 15.2 Gb/s and 13.8 Gb/s over a 6-mm on-chip interconnect for PRBS7 and PRBS31 patterns, respectively. It demonstrates the fastest normalized speed among prior arts: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$316\text{Gb}/\mathrm{s}\cdot {\text{mm}}^2/{\mu }^2$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$286.9\text{Gb}/\mathrm{s}\cdot {\text{mm}}^2/{\mu }^2$</tex> for PRBS7 and PRBS31. At 15.2 Gb/s with PRBS7, it achieves 468 fJ/b energy efficiency and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\text{FoM}}_{\text{dB}}$</tex> of 23.5 fJ/b/dB.

Spatial charge trap engineering using amorphous boron nitride (BN) energy barrier for 3D V-NAND flash memory device is presented. A 1 nm thick BN layer is inserted within a silicon nitride (SiN) charge trap layer (CTL) using an In-situ ALD process. The CTL with the BN barrier located at an optimized position showed clear advantages in memory window and charge retention. The advantage of using the BN barrier becomes even more apparent when the CTL is scaled down to 4 nm, having more than 20% larger memory window, 44% improvement in hole retention, and more than 10 times faster erase speed compared to the same thickness of pure SiN CTL, which helps to advance XY-scaling in 3D V-NAND flash devices.