Papermog

OBJECTIVE: Brain computer interfaces (BCIs) based on selective auditory attention aim to restore communication by decoding selective attention from auditory evoked potentials. Clinical translation of such BCIs requires maintaining sufficient decoding performance despite brain state non stationarity.Approach: We compared classifiers across four evaluation settings: offline baseline classifiers using shuff...

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Quantum TechnologyOpenAlex2026-07-03Verified (89)

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Spin-photon qubits for scalable quantum network

Md Sakibul Islam, Kuldeep Singh, Yunhe Zhao et al.

Abstract Solid state quantum light sources offer a scalable pathway for interfacing stationary spin qubits with flying photonic qubits, forming the backbone of future quantum networks. Telecom band spin photonic qubits, operating in the 1260–1675 nm wavelength range, are particularly well suited for long distance quantum communication due to minimal loss in standard optical fibers. Achieving scalability, however, ...

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AI & CognitionOpenAlex2026-07-03Preprint (50)

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STF-KernelSHAP: A Model-Agnostic Space–Time–Frequency Shapley Framework for Physiologically Informed EEG Explainability

Diego Armando Pérez-Rosero, Andres Camilo Lopez-Boscan, Andrés Marino Álvarez-Meza et al.

Interpretability is essential for deploying deep learning (DL) models in electroencephalography (EEG) based neurotechnology, particularly in brain–computer interfaces and clinical decision support settings. Existing post hoc explainable artificial intelligence (XAI) methods often yield single domain attribution maps, limiting their capacity to characterize the joint spatial, temporal, and spectral structure of EEG...

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AI & CognitionOpenAlex2026-07-03Skeptical (34)

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RGB-Style Input Representations for EEG: Evaluating Spatial Concatenation Versus Band-Wise Stacking in Deep Emotion Recognition

Xin Zhang, Ye Li, Fei Pi et al.

Background/Objectives: Electroencephalography (EEG) is widely applied in emotion recognition. Integrating diverse frequency and spatial features to improve performance remains a major challenge. Methods: This paper proposes two preprocessing methods to map EEG signals into image style representations. These methods preserve the spatial topology and enable effective feature extraction using convolutional neural net...

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AI & CognitionOpenAlex2026-07-03Preprint (55)

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Noninvasive Intelligent Brain‐Metasurface System for Real‐Time Electromagnetic Wave Manipulation

Xiao Hai, Ruiyang Shen, Xiaoyi Wang et al.

ABSTRACT A noninvasive intelligent brain–computer interface (BCI) is integrated with a reconfigurable metasurface to establish a direct neural‐to‐electromagnetic transduction pathway for real‐time, multimodal wave control. By combining convolutional‐neural‐network‐based decoding of motor‐imagery electroencephalography (EEG) signals with threshold‐based recognition of electrooculography (EOG) commands, the proposed...

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Space & PhysicsOpenAlex2026-07-03Verified (85)

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Experimental Data Management System for Long-Term Fusion Experiments

M. Emoto, Hideya NAKANISHI, Masanobu Yoshida et al.

Abstract The LHD experiment started in 1998 at the National Institute for Fusion Science and has continued for nearly 30 years. The experimental data obtained in these experiments are stored as physical data in the Kaiseki Data Server and made available to the public using an open data server. Through the experience of developing and operating this server, this paper discusses the problems that can occur in data m...

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AI & CognitionOpenAlex2026-07-03Skeptical (20)

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AI-Based Personalization of 3D-Printed Hand Exoskeletons

Dariusz Mikołajewski, Jakub Kopowski, Zbyszko Królikowski et al.

This article discusses advanced artificial intelligence (AI) based strategies for the design and personalization of three dimensionally (3D) fabricated hand exoskeletons, with a focus on adaptive, data driven methodologies. It highlights the crucial role of intelligent personalization in improving user comfort, functional performance, and rehabilitation outcomes, particularly in medical and care settings. The prop...

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Space & PhysicsOpenAlex2026-07-03Verified (84)

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Riemannian manifold dynamic attention fusion network for motor imagery EEG decoding

Dingming Wu

A notable challenge encountered by motor imagery decoding algorithms utilizing electroencephalography (EEG) signals is the substantial redundancy and inadequate geometric representation of spatiotemporal features, which stem from the volume conduction effects inherent to the human head. This phenomenon can obscure essential information regarding motor intentions with noise or non discriminative features. Although ...

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Bio-EngineeringOpenAlex2026-07-03Preprint (65)

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Predictions of high field side lower hybrid current drive in positive and negative triangularity DIII-D-class and ARC-class plasmas

G. Rutherford, Paul T Bonoli, Collin Dunn et al.

Abstract Negative triangularity (NT) offers a potential solution to the high heat fluxes incident on the divertor of reactor class tokamaks by blocking access to H mode. An important step in assessing how NT may scale to a reactor is determining the behavior of existing current drive schemes in NT. Lower hybrid current drive (LHCD) is an attractive technology due to its high efficiency. The effect of triangularity...

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AI & CognitionOpenAlex2026-07-03Preprint (58)

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Explainable common spatial pattern to improve the design of brain-computer interfaces

Kais Belwafi, Sofien Gannouni, Bouziane Brik et al.

Motor Imagery (MI) based Brain Computer Interfaces (BCIs) depend on interpretable and robust feature extraction methods. Although Common Spatial Pattern (CSP) algorithms effectively discriminate neural patterns, their practical and clinical utility remains limited by the lack of explainability and sensitivity to noise and inter subject variability. This paper introduces a framework known as balanced and explainabl...

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Space & PhysicsOpenAlex2026-07-03Preprint (45)

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INTEGRATED HIERARCHOLOGICAL REPORT OF PART OF THE BOOK

Karl Petrič

Executive Summary The work proposes a single‑principle, process‑oriented ontology in which difference is the primordial generator of all ontological categories. From difference arise potential, which gives rise to processes; processes generate compensators that maintain a measure within which an identity can persist. The cycle then closes as micro‑transitions create new differences, restarting the loop. The author...

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AI & CognitionOpenAlex2026-07-03Preprint (55)

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Perspective Chapter: Monte Carlo for Artificial Intelligence, Foundations and Emerging Trends

Fatima Zahra Chafi

This chapter provides a structured overview of Monte Carlo Methods (MCMs) and their expanding role in artificial intelligence. It introduces the fundamental principles of stochastic sampling, probabilistic modeling, and simulation, along with core Monte Carlo algorithms used for optimization, inference, learning, and decision making under uncertainty. It traces the evolution of these techniques from classical scie...

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AI & CognitionOpenAlex2026-07-03Preprint (68)

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Learning-related population dynamics in right and left dorsal premotor cortex during typing skill acquisition

Hiroaki Hashimoto, Justin Jude, Hadar Levi-Aharoni et al.

Advances in intracortical brain computer interface (BCI) technology have enabled increasingly sophisticated communication paradigms, including for decoding intended speech and touch typing. However, the methods by which intracortical neural population dynamics are engaged during practice related skill acquisition in humans remain poorly understood. Here, we examined learning related changes in neural activity duri...

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AI & CognitionOpenAlex2026-07-03Verified (75)

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Role of Artificial Intelligence in Pharmaceutical Sciences: Recent Advances, Applications, Challenges, and Future Perspectives

Avril Candida Mathias, K Kavitha, N Venuka Devi et al.

Background The global expansion of COVID 19 has made the development of pharmacological drugs a more pressing and important area of study. The expensive and time consuming process of developing new drug molecules to treat any disease continues unabated. Over the past 20 years, artificial intelligence has substantially improved the performance of industrial and service systems. Expert systems, a rapidly developing ...

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Quantum TechnologyOpenAlex2026-07-03Skeptical (28)

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Multi-qubit entanglement and quantum processing within ququart

Yulin Chi, Yuhong Huang, Haiyu Ding et al.

Abstract Quantum technology, leveraging non classical characteristics, enables breakthroughs in computation, communication, and metrology. Entanglement generation is fundamental. However, efficiently creating large scale entanglement remains challenging. Using 2 n physical modes within a single particle has been exploited for encoding n qubit entanglement, yet this approach is believed to be unscalable. Here, usin...

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Space & PhysicsOpenAlex2026-07-03Preprint (40)

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A Unifying Perspective on Kolmogorov Cascades in Solar Wind Plasma Turbulence

N. H. Bian, Gang Li

Abstract It is argued that Kolmogorov cascades, including those affecting space weather in the solar wind plasma, can effectively be described by realizations of a continuous time random walk. The nature of the random walk is ruled by the nonlinearity of the structure function scaling exponent characterizing intermittency and multifractality. The scale dependent probability density functions satisfy the Montroll–W...

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Space & PhysicsOpenAlex2026-07-03Skeptical (5)

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A Classical Vacuum Spin Model for Propellantless Propulsion

Ioannis Xydous

A classical model of the vacuum as a continuum of massless spin carriers is developed and applied to propellantless propulsion. In this framework, a spacecraft imposes a standing electromagnetic wave with a position‑dependent, time‑varying phase gradient. The vacuum responds by generating a compensating spin current, and the reaction to this induced flow produces thrust without expelling propellant. The model repr...

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Bio-EngineeringOpenAlex2026-07-03Verified (94)

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Active devices and systems for closed-loop neuromodulation

Yi-Xiang Wang, Huize Xue, Li J et al.

Neuromodulation has become a central topic in neuroscience and biomedical engineering, as it provides powerful means to interrogate and regulate neural activity and offers promising therapeutic strategies for a wide range of neurological disorders. Conventional neuromodulation approaches predominantly rely on electrode based electrical stimulation or remote physical stimuli, including optical, chemical, magnetic, ...

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Quantum TechnologyOpenAlex2026-07-03Verified (94)

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The finite amplitude method for nuclear collective motion

Takashi Nakatsukasa, Nobuo Hinohara

Abstract The linear response theory is a powerful tool to study properties of quantum many particle systems. It uses a one body perturbation field (as a pump) and measures a change in another (or the same) one body observable (as a probe). In the context of time dependent density functional theory (TDDFT), the primary target of the linear response is the density response. The finite amplitude method (FAM) signific...

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AI & CognitionOpenAlex2026-07-02Skeptical (30)

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Concurrent control of natural and robotic limbs through a tactile-encoded brain-computer interface

Tianyu Jia, Xingchen Yang, Ciarán McGeady et al.

Abstract Brain computer interfaces (BCIs) promise to extend human movement capabilities by enabling direct neural control of supernumerary effectors, yet integrating augmented commands with multiple degrees of freedom without disrupting natural movement remains a key challenge. Here, we propose a tactile encoded BCI that leverages sensory afferents through a tactile evoked P300 paradigm, allowing reliable decoding...

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