Brain-computer interfaces

Purpose: Cancer is one of the serious diseases of our time, claiming the lives of many people annually. Numerous experiments have been conducted to treat various types of cancer, but most have not yielded satisfactory results. The aim of this article is to explore the topic of cancer treatment and the destruction of cancerous tumors by sending signals using neuroprostheses.
Method:
In this method, we use healthy cells from the region containing cancer as a sample. We then determine their frequency and continuously send signals to the cancerous area using a
prosthesis or electrode, thereby strengthening the healthy cells. Alternatively, we use cancer cells as a sample, determine their frequency, and send signals to the cancerous area to destroy the cancer cells. We have explained both methods in detail.
Result:
Based on the experiments conducted in this field (of which we have provided many examples) and the existing devices and methods, we can easily sample the cells, obtain their frequency, and simulate these frequencies using the devices and methods we have introduced.
Conclusion:
According to the findings of this research and its combination with the phenomena of resonance and cymatics, we can treat cancers by sending signals to the cancerous area using
prostheses or electrodes. This method can weaken and eliminate cancer cells.

is a new, overnight work in which four performers fall asleep while wearing custom designed EEG sensors which monitor their brainwave activity. The data gathered from the EEG sensors is applied in real time to different audio and image signal processing functions, resulting in continuously evolving multichannel sound environment and visual projection. This material serves as an audiovisual description of the individual and collective neurophysiological state of the ensemble. Audiences are invited to experience the work in different states of attention: while alert and asleep, resting and awakening.

In a study published in PNAS Nexus, Alexander Genevsky and colleagues explore neuroforecasting, blending neuroscience, technology, and metaphysics.  Using fMRI, neuroforecasting taps the nucleus accumbens to predict collective behavior, its reward signals echoing crowd dynamics even in isolation, hinting at a deep biological and vibrational unity.  The paper considers nanotechnology and genetic engineering, graphene nanoribbons, CRISPR, and speculative interfaces like NeuroLattice, as tools to map and amplify this resonance, potentially tying consciousness to universal rhythms.  It ties in serpentine locomotion as a biological and technological concept, alongside Islamic views on spiritual unity, jinn, and tawheed, framing resonance as both scientific and metaphysical.  Ultimately, it suggests these advances could blur the lines between self and cosmos, reflecting humanity’s pursuit of oneness.

One of the main challenges for current neurosciences is to comprehend how information is encoded in neural networks. One technical approach to study distributed multi-neuronal activity patterns is recording from brain slices with microelectrode arrays (MEAs). Here we describe two new MEAs which are optimized to record network activity in the main cell layers of the rodent hippocampus. The anatomical structure and variance of the hippocampal formation was mathematically approximated. Gold MEAs were designed such that microelectrodes followed the main hippocampal cell layers in areas connected by Schaffer collaterals (CA3-CA1) and by mossy fibers (DG-CA3), respectively. This electrode distribution allows recording action potentials from 58 different points along the hippocampal loop, and is optimized for defining the origin and propagation of physiological and pathological activity patterns in these networks. Functionality of MEAs was validated with three different patterns: (i) spontaneous network activity; (ii) synaptic input-output curves; (iii) response to electro-chemical stimulation triggered by a bipolar stimulus and by perfusion of gabazine. Our results show that these matrixes are well suited to characterize the spatiotemporal network activity in densely packed cell layers. Specifically, they allow for accurate localization of the area(s) of origin and the pathway(s) of propagation of compound network activity.

El desarrollo de las Interfaces Cerebro-Máquina (BCI) ha surgido como una tecnología revolucionaria que permite la comunicación directa entre el cerebro humano y dispositivos inteligentes. Este estudio evalúa la efectividad de diversas técnicas de Aprendizaje Automático (ML) y Aprendizaje Profundo (DL) para la clasificación y detección de potenciales relacionados con eventos P300 en señales EEG, con un enfoque en aplicaciones de habla interna. Se implementó un prototipo modular de BCI utilizando conjuntos de datos públicos de EEG, y las señales preprocesadas fueron analizadas para identificar los métodos de clasificación más eficaces.

Los resultados demuestran que las técnicas de preprocesamiento, como la combinación de filtros Notch y paso banda con Análisis de Componentes Independientes (ICA), mejoran significativamente la calidad de las señales al reducir el ruido y preservar patrones relevantes. Entre los métodos de clasificación evaluados, las Redes Neuronales Convolucionales (CNN) con Transformada de Wavelets alcanzaron la mayor precisión (.91), superando a los modelos tradicionales de ML como Máquinas de Soporte Vectorial (SVM) y Bosques Aleatorios (Random Forest). Esto destaca la importancia de analizar tanto los dominios de tiempo como de frecuencia para la detección precisa de señales P300.

Este estudio subraya el potencial de los modelos avanzados de ML y DL en la mejora de los sistemas BCI para el reconocimiento de habla interna, con prometedoras implicaciones para tecnologías de asistencia y sistemas de comunicación para personas con discapacidades del habla o motoras.

The governance of data is a vital component for every organization, but it is of utmost significance in situations when there are a significant number of users both within the organization and beyond its boundaries or boundaries. To ensure that only authorized users are able to access the required data, it is essential necessary to implement governance policies that are both effective and efficient. Although many organizations have governance boards, problems continue to exist, which results in weaknesses inside the system. This study presents an inquiry into the ways in which modern technologies, such as artificial intelligence and machine learning, have the potential to improve data governance procedures within enterprises. Specifically, the study focuses on how these technologies can improve operations. There is an analysis of the limitations that are inherent in conventional methods, as well as insights into the tactics that might be applied to improve governance practices, which are both included in the framework of this debate.

The advent of AI-powered computer-brain interfaces (CBIs) marks a transformative era, where technology extends human cognitive and physical abilities far beyond previously imagined limits. CBIs, driven by cutting-edge advancements in artificial intelligence, are poised to reshape how humans interact with machines, their environment, and even themselves, blurring the boundaries between organic and synthetic intelligence. These interfaces enable direct communication between the brain and external devices, offering unprecedented possibilities for cognitive enhancement, motor function restoration, and human-computer interaction.
This study examines how artificial intelligence (AI)-driven CBIs are expanding the notion of what is possible for humans by providing new avenues for improved cognitive function, communication, and healthcare. By providing accurate, real-time feedback, AI-powered CBIs are transforming mental health therapies, neuroprosthetics, and rehabilitation in the healthcare industry. These interfaces can potentially improve learning, memory, and decision-making in cognitive domains. The research also explores how artificial intelligence (AI) can be combined with neural decoding methods to enable smooth interactions between humans and machines. 
A rigorous examination is given to ethical issues such data privacy, autonomy, and fair access, since these technologies give rise to serious worries about the future of human identity and social divisions. The possibility for cognitive differences complicates the future terrain of human-machine integration, particularly between those who can afford these technologies and those who cannot. The future potential of CBIs is also highlighted in this work, including neural nanotechnology, quantum computing for neural decoding, and non-invasive brain-machine interfaces. These developments could improve the functionality and accessibility of CBIs.
In the end, the study looks at the wider societal ramifications of reintroducing AI to humanity and asks whether such a deep connection with technology would put our conception of what it means to be human and our ethical obligations into jeopardy. Combining AI with CBIs opens up new possibilities for technical development and human progress, but it also brings with it a number of difficulties.

Objective: to identify theoretical and legal problems associated with the steadily increasing spread of digital and biotechnologies’ development products; to assess the risks associated with this process that can change the position of a human in the society of the future; to develop and substantiate proposals to minimize risks and eliminate the identified problems through legal regulation.

Methods: the research is based on the methods of generalization of scientific and technical information and theoretical analysis used while studying the source materials; axiological and systematic approaches; the formal legal method and, in addition, methods of legal forecasting, primarily extrapolation, which made it possible to highlight the prospects for reforming law due to technological expansion.

Results: include a description of the dynamics of digital and bio-technologies’ development; a description of the changing social landscape with the emerging new types of entities that can affect the actual status of a human; a classification of risks threatening people due to the digital and biotechnologies development; a list of tasks whose solution based on law will help to eliminate, postpone or, at least, significantly reduce the severity of problems by increasing the time limit allotted to humanity to comprehend a number of conceptual points. The author presents arguments substantiating the need to develop special legal regulation in relation to new types of entities, the appearance of which becomes an inevitable result of the mentioned technologies’ development.

Scientific novelty: consists, first, in a comprehensive study of the development of interconnected groups of digital and biotechnologies, taking into account their increasing convergence; and, second, in the formulation of legal problems that need to be resolved due to the potential emergence of new types of entities with cognitive functions and capable of having a targeted intellectual impact on the environment and legal entities.

Practical significance: it is present in the answers given to the formulated legal questions, which contain suggestions and recommendations on the necessary adjustment of legal regulation and focus the attention of legal scholars on the problems arising from the pace and vectors of scienceconsuming technologies’ development.

Presently, the in vitro recording of intracellular neuronal signals on microelectrode arrays (MEAs) requires complex 3D nanostructures or invasive and approaches such as electroporation. Here, it is shown that laser poration enables intracellular coupling on planar electrodes without damaging neurons or altering their spontaneous electrophysiological activity, allowing the process to be repeated multiple times on the same cells. This capability distinguishes laser-based neuron poration from more invasive methods like electroporation, which typically serve as endpoint measurement for cells. It is demonstrated that planar MEA electrodes, when combined with laser cell optoporation and live cell staining, can record spontaneous intracellular signaling from primary neurons in vitro. This approach allows for the detection of attenuated signals resembling positive monophasic intracellular action potentials. Recordings after laser optoporation also reveal subthreshold signals such as post-synaptic potentials that are essential for assessing neuronal network plasticity and connectivity. Moreover, the noninvasiveness of the process enables repeated intracellular recordings over multiple days from the same cells.

Brain-to-brain communication (B2B) is an emerging field that explores the possibility of direct thought exchange between individuals using neuroinformatics, brain-computer interfaces (BCI), and artificial intelligence (AI). Recent advancements in neural decoding, functional brain mapping, and transcranial stimulation have demonstrated the feasibility of limited brain-to-brain information transfer. This paper reviews key experimental breakthroughs, including human and animal studies, and proposes a neuroinformatics-based framework for structured thought exchange. We also discuss the potential applications of B2B communication in medicine, education, and collaborative intelligence. Furthermore, ethical concerns related to privacy, security, and neural compatibility are addressed. While challenges remain, continued research in neuroscience and AI could revolutionize communication by enabling seamless cognitive interaction.

Abstract

The human brain's remarkable plasticity renders it susceptible to various external influences that can alter its structure and function. This comprehensive review examines the concept of "brain hacking," referring to the manipulation or modification of neural processes through external means, and explores how factors such as social isolation and environmental stressors contribute to these changes. By synthesizing current neuroscientific literature, we aim to elucidate the mechanisms underlying brain hacking and the role of isolation in enhancing neural vulnerabilities.

Keywords:Brain hacking, neural plasticity, social isolation, environmental stressors, neurosecurity, neuroethics

Brain-machine interfaces can provide intuitive control of robotic prosthetic legs, exoskeletons, and other assistive technologies. However, a grand challenge is the design of the neural decoding algorithms used to transform brain signals into control commands. In this study, we explored the best combination of signal processing, feature extraction, machine learning to decode user intent based on electroencephalography (EEG) data. We used a large-scale dataset recorded from a 64-electrode EEG headset and two 3D accelerometers on the feet with 15 subjects. We systematically compared artifact subspace reconstruction, surface laplacian filter, independent component analysis, and common spatial patterns as the signal processing and feature extraction algorithms alongside both classical machine learning and deep learning classifiers. Our convolutional neural network in combination with a surface laplacian filter and independent component analysis achieved the highest classification accuracy during motor imagery with an average F1-score of 88.3% across all subjects. Additionally, we found that artifact subspace reconstruction improved intent decoding when used with a long short-term memory classifier. These findings could help neuroscientists develop next-generation AI-powered brain-machine interfaces to control robotic leg prostheses and exoskeletons that assist and physically interact with humans.

Brain-machine interfaces (BMIs) using electroencephalography (EEG) have the potential to assist individuals with paralysis or neuromuscular diseases. However, the development of EEG decoding models remains a challenge due to inter-subject variability. This study proposes a novel contrastive learning approach for source selection to improve cross-subject EEG decoding. The proposed method introduces a ground truth generation technique using under-parameterized feature extractors to measure domain similarity between subjects. To evaluate the effectiveness of our approach, two studies were conducted using the PhysionetMI dataset which includes 109 subjects and 22 hours of recordings. The first study, employing a "same subject" objective, was outperformed by our global model benchmark trained on all subjects. The second study, using a "same domain" objective which was enabled through the ground truth generation, achieved significant improvements, with a 10% increase in low-calibration accuracy and a 4.8% increase in zero-calibration accuracy compared to the global model. Notably, the zero-calibration accuracy was only 1.2% lower than that achieved by fine-tuning the global model on the target subject. These findings demonstrate the effectiveness of our contrastive learning approach for source selection in enhancing subject-invariant EEG decoding. The implications of this research are broad, as reducing the need for extensive subject-specific calibration can make brain-machine interfaces more accessible and feasible across a wide range of applications.

La foudre, phénomène naturel à effets directs et indirects, apporte souvent des désastres. A Madagascar, à cause des coûts élevés des parafoudres et paratonnerres, les gens pratiquent plutôt la prévention par prévision traditionnelle. Notre objectif est d'apporter une contribution à l'amélioration de la prévision des foudres. Cette étude comporte deux phases : la phase de conception et de réalisation du système de mesure, puis la phase de mesure du champ atmosphérique et de prévision d'arcs-en-retour. Le système de mesure réalisé a été utilisé pour mesurer des champs électriques à Ambohibao (Antananarivo), à différentes dates de la saison pluvieuse 2013-2014, pendant les formations d'orages et pendant les activités orageuses. L'utilisation « capteur-convertisseur analogique/numérique » nous a servi à tracer la variation du champ électrique atmosphérique pour les différentes phases d'orages. Elle a également servi à faire une prévision de l'activité orageu...

operational efficiency in resource-constrained environments. By processing real-time
neural signals, the study explores how BCI can support decision-making processes,
automate repetitive tasks, and monitor employees’ cognitive performance. The research
also addresses the technical, ethical, and economic challenges associated with BCI
implementation and proposes innovative solutions for integrating this technology
effectively and sustainably. Expert opinions on the practical applicability of BCI and its
potential impact on institutional improvement.

In a groundbreaking exploration at the intersection of neuroscience and technology, this study delves into the innovative integration of Brain-Computer Interfaces (BCIs) with brain organoids. By harnessing advanced machine learning techniques, we aim to elevate our understanding of neural activity. Our research introduces a hybrid system that not only bridges the gap between in vitro models and in vivo neural dynamics but also has the potential to revolutionize both neuroscience research and medical applications. Through this integration, we decode complex neural signals, unveiling new insights into brain functionality and pioneering therapeutic strategies for neurological disorders.

Single channel EEG systems are very useful in EEG based applications where real time processing, low computational complexity and low cumbersomeness are critical constrains. These include brain-computer interface and biofeedback devices and also some clinical applications such as EEG recording on babies or Alzheimer's disease recognition. In this paper we address the problem of eye blink artifacts detection in such systems. We study an algebraic approach based on numerical differentiation, which is recently introduced from operational calculus. The occurrence of an artifact is modeled as an irregularity which appears explicitly in the time (generalized) derivative of the EEG signal as a delay. Manipulating such delay is easy with the operational calculus and it leads to a simple joint detection and localization algorithm. While the algorithm is devised based on continuous-time arguments, the final implementation step is fully realized in a discrete-time context, using very classical discrete-time FIR filters. The proposed approach is compared with three other approaches: (1) the very basic threshold approach, (2) the approach that combines the use of median filter, matched filter and nonlinear energy operator (NEO) and (3) the wavelet based approach. Comparison is done on: (a) the artificially created signal where the eye activity is synthesized from real EEG recordings and (b) the real single channel EEG recordings from 32 different brain locations. Results are presented with Receiver Operating Characteristics curves. The results show that the proposed approach compares to the other approaches better or as good as, while having lower computational complexity with simple real time implementation. Comparison of the results on artificially created and real signal leads to conclusions that with detection techniques based on derivative estimation we are able to detect not only eye blink artifacts, but also any spike shaped artifact, even if it is very low in amplitude.

Abstract: Brain tumor is inherently serious and life-threatening disease. Brain tumor is an abnormal growth of cells within the brain or inside the skull, which can be cancerous or noncancerous. Early detection and classification of brain tumors is very important in clinical practice. With recent development in the medical engineering and instruments, EEG instruments are able to record the brain electric activities with high accuracy, which establishes EEG as a primary tool for diagnosing the brain abnormalities. This paper represents earlier detection of brain tumor using artificial neural networks (ANNs).EEG signals carry the information of human brain. But EEG signal is contaminated with artifacts. These artifacts are removed by adaptative filtering. Then the spectral analysis method is applied for extracting generic features in an EEG signal. Fast Fourier Transform for spectral analysis is used to separate the signal features present in noise. The clean EEG is obtained and fed t...

The future of human-computer interaction is poised for a revolutionary shift. While current approaches often rely on physical gestures or invasive methods, this paper explores the potential of non-invasive neural interfaces. By leveraging thought control, eye movements, and a completely futuristic technology, we propose a seamless and intuitive connection between humans and machines. This vision not only eliminates the need for physical gestures but also provides a safer and more accessible alternative to invasive technologies. This paper delves into the technical, ethical, and societal implications of such innovations and outlines the potential pathways for their development and adoption.

Die Schriftenreihe Interdisziplinäre Studien zur Germanistik bietet Wissenschaftler*innen der Germanistik weltweit eine Plattform zu neuen, zeitgenössischen und zukunftsweisenden Studien an. In Anbetracht der Komplexität der heutigen Forschungslandschaft wird davon ausgegangen, dass die deutsche Philologie, als eine der zentralen Disziplinen der Geisteswissenschaften, erheblich von einem dialogischen Austausch mit anderen akademischen Bereichen profitieren kann. Die anonym begutachteten Bände dieser Reihe bringen demzufolge die Germanistik in Austausch mit der Linguistik, Semiotik, Literaturwissenschaft, Kulturwissenschaft, Film-und Medienwissenschaft, Geschichte und Philosophie, u.v.a. Es wird daher als essenziell erachtet, neuen und relevanten Forschungsansätzen Zugang zu verschaffen, um den akademischen Diskurs nachhaltig zu bereichern. Darüber hinaus wird ein interdisziplinärer Austausch gefördert, indem Werke veröffentlicht werden, die unterschiedliche wissenschaftliche Ansätze und Methoden integrieren. Dadurch soll die Zusammenarbeit zwischen den verschiedenen Disziplinen gestärkt und neue Synergien geschaffen werden. Ein zentrales Anliegen der Reihe besteht demnach darin, die Sichtbarkeit und Zugänglichkeit von Forschungsergebnissen zu erhöhen, indem ein breites Publikum angesprochen wird. vii CONTENTS PREFACE / VORWORT .

IntraCavi utilizes natural pathways such as the oral cavity, palate, nasal mucosa, ear canal, and eye muscles to enable comprehensive neurostimulation. This method combines electrical impulses with vibration mechanisms and the integration of Artificial Intelligence (AI) for real-time adjustment. The goal is to enhance brain oxygenation, promote neuronal activation, and alleviate conditions such as pain, Parkinson's symptoms, stroke, or Alzheimer's, among others. By employing biocompatible materials and advanced control systems, IntraCavi provides a non-invasive and personalized approach to neurological therapy. This article examines the advantages of this approach over existing technologies, such as the PoNS system and invasive methods like Neuralink, and discusses future applications in both clinical and professional settings, including cognitive enhancement and rehabilitation.

Keywords: Neurostimulation, IntraCavi, Non-invasive technology, Artificial Intelligence, Vibration therapy, Cognitive enhancement, Parkinson’s relief, Biomedical Engineering, Personalized medicine.

License: This work is licensed under a Creative Commons Attribution (CC BY) license.

Questo lavoro esplora il campo delle neuroscienze computazionali e delle simulazioni cerebrali, con un focus sulle tecnologie emergenti, le applicazioni in ambito medico e le implicazioni etiche. Attraverso l'analisi delle neuroscienze, dell'intelligenza artificiale e delle biotecnologie, il documento esamina come i modelli matematici e le simulazioni possano migliorare la comprensione dei processi cognitivi, neurologici e comportamentali. Viene discusso l'uso di simulazioni cerebrali nella diagnosi e nel trattamento delle malattie neurologiche, con particolare attenzione a patologie come l'Alzheimer e il Parkinson. Inoltre, il lavoro affronta i rischi di abuso delle tecnologie, le sfide relative alla privacy e al controllo delle informazioni cerebrali, nonché le questioni morali e sociali legate all'utilizzo delle simulazioni cognitive. Infine, il paper considera le prospettive future di queste tecnologie, analizzando il loro impatto sulla medicina, sulla società e sull'etica scientifica.

Contemporary culture is imbued both implicitly and explicitly with the ideas of transhumanism. Using principles such as (self-) optimisation and technologisation, transhumanism suggests the idea of constantly transcending the boundaries of immediate physical and psychological human existence. Self-optimisation is no longer just about improving fitness and nutrition – for example, through devices we wear, such as smartwatches or glasses, or beauty, or physical/cognitive and emotional/social performance mediated through coaching sessions. The so-called human enhancement means self-improvement in which something new – through genetic, biochemical, technical/technological improvement – is added to what is naturally given and learnt; something that goes beyond the human being. Transhumanist thinking is thus based on an unshakeable faith in scientific and technological progress, and on the belief that genetic, biotechnological, neuroscientific, and robotic research will soon overcome natural limits and improve the physical and cognitive capacities of the human race to such an extent that biological/ evolutionary limits will be broken, and we will be freed from ageing, disease, suffering, poverty, and ignorance. The authors of this monograph attempt to critically evaluate some of the outlined transhumanist ideas on a theoretical and practical level, using a specific worldview approach that is open to transcendence. More specifically, it is a religious (Christian/Catholic) approach based on moderate biblical anthropocentrism and theonomic ethics. This approach recognises an intrinsically universal and unchanging order of moral goods. Its recognition leads to a wise conception of the world in general, and its implementation leads to specific prudent choices, characterised, among other things, by predictability, foresight and caution in the face of contemporary transhumanist ideas and scientific and technological achievements. At the same time, this approach respects people’s abilities, limitations, and vulnerabilities. It allows for a form of education that enables the human being to develop in his or her nature in an integral and appropriate way. In this complex perspective, the authors of the monograph aim to present and justify how scientific/technological achievements should be adapted to the individual and his or her real well-being in order to serve the common good of the whole of humanity and also of future generations. The degree of humanity is determined by the extent to which a person and a society can recognise not only their strengths but also their limitations (e.g. poverty, physical and mental disability, ageing, illness, death), how they can integrate and embrace the weaker and the suffering with appropriate care, without violence in the form of exclusion or excessive concern for them. The current scientific, technical, and technological developments should help people and be at the service of all humanity to build a peaceful and secure coexistence here on Earth in a spirit of social brotherhood and just friendship in this globalised world.

Accurate neural decoding of brain dynamics remains a significant and open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies evaluating different combinations of state-of-the-art algorithms for motor neural decoding to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that help inform the design of next-generation neural decoding algorithms for brain-machine interfaces used to interact with and control robots and computers.

In today's interconnected global economy, the importance of tax and risk compliance cannot be overstated. Businesses face an array of challenges-from regulatory changes and economic uncertainties to the complexities of digital transformation. At the core of these challenges lies the necessity for businesses to ensure adherence to tax laws and mitigate risks effectively. This article delves into why tax and risk compliance matters, explores its impact on business valuation, and offers actionable insights for organizations aiming to stay ahead in a competitive marketplace. Understanding Tax Compliance Tax compliance refers to the act of adhering to tax laws and regulations, ensuring that all tax obligations are accurately calculated, reported, and paid on time. For businesses, tax compliance extends beyond paying income taxes. It includes complying with payroll taxes, value-added taxes (VAT), customs duties, and other obligations based on jurisdiction.

【Abstract】 The ethical governance of brain-computer interface (BCI) research in mental disorders is crucial to effectively prevent ethical risks of technology, and promote responsible innovation by various stakeholders. As a key research priority of China′s Brain Project, the applications of BCI for treating mental illness, especially in treatment-refractory patients, have become a research hotspot. This expert consensus is formed by multidisciplinary from psychiatry, neurosurgery, engineering, computer and information science, ethics, philosophy of science and technology, psychology, law, etc. Based on several rounds of discussions and the Delphi survey, the consensus establishes the basic principles and key ethical review considerations for the ethical governance of BCI research in psychiatric disorders. The consensus proposes that BCI-related clinical research should follow the principles of scientific criteria, beneficence, autonomy, minimal

The pursuit of a unified theory that captures the intricacies of the brain and mind continues to be a significant challenge in theoretical neuroscience. This paper presents a novel, triune framework that utilizes the concept of collective biased random walk (cBRW). Our approach strives to transcend biological specifics, offering a high-level abstraction that remains general and applicable across various neural phenomena. Despite the solid traditional foundation of computational neuroscience, the intricate delicacy of neural processes calls for a renewed probabilistic approach. We aim to utilize the intuitive nature of probability concepts —such as the probability of localization and state, and uniform probability distribution— to study the stochastic organization of electric charges and signals in the brain. This electrophysiological intricacy emerges from the seemingly paradoxical reality that tiny electric events, while random, collectively give rise to predictable, long-range oscillations. These oscillations manifest in three groups of activation states. Our framework categorizes the brain as a triune system, accommodating classical, semiclassical, and non-classical interpretations of both probabilistic phenomena and cBRW models, alongside three groups of states. We conclude that by appreciating, rather than overlooking, the tiny random walks of electric charges and signals in the brain, we can gain a triune mathematical foundation for theoretical brain science, the powerful capabilities of this organ, and the electromagnetic interfaces we can develop. Manuscript received: 30-10-2023. Revised: 19-06-2024. Accepted: 21-08-2024. RESEARCH ARTICLE. Keywords. Brain States, Brain Theory, Brain Electrophysiology, Neural Computations, Electroencephalography, Non-Classic Brains. MAIN Abbreviations: BRW: Biased Random Walks, cBRW: Collective Random Walks, P(.): Probability, Pl(x): Probability of localization at x, Ps(b): Probability of state b, RW: Random Walk. OTHER Abbreviations: 1D: one Dimension, 2D: two Dimensions, DWI: diffusion weighted imaging, E(.): Expectancy, EEG: Electroencephalography, MEG: Magnetoencephalography, MRI: Magnetic Resonance Imaging, NIRS: near-infrared spectroscopy, TES/TMS: electric/magnetic stimulation, Var(.): Variance. International Group of Neuroscience, Neuroengineering and Neurophilosophy, IGN(S,E,P). Colombia, US, India, Europe.

In the states, penetration of mobile platforms at an equal speed is completely changing things around for people and enterprises making life easy and accessible. This move, however has new baggage of cyber threats associated with it that made mobile devices the softest targets for an attack. The present study sheds a light on the strategies and inventions demanded in mobile platform cybersecurity evolution after analyzing contemporary literature to uncover latent loopholes inside U.S. Mobile platforms, namely application security; data encryption, and user authentication unit. More report on Artificial Intelligence (AI), Machine Learning(ML) and Blockchain as the technology can be used to have better security in mobile. Exhaustive evaluation of case studies and trends emerging in the market is presented to understand how these technologies have been proven beneficial across various use cases. This highlights again how crucial it is to take a multi-layered approach when tackling mobile security, combining innovative technologies with strong policies and effective user education. The paper concludes by providing specific strategic recommendations for actors in the mobile ecosystem and highlights how continuous innovation and collaboration are key to stay one step ahead of cybercriminals as they adapt.

As online banking continues to grow and change, solid fraud detection is a must in order for financial data not be lost and instead live up to the significance of trust from customers. Cybersecurity at the Next Level: Advanced Cyber Security for Fraud Detection and Prevention in U.S. Online Banking Systems We assess the benefits of IVF with a variety of new technologies, which include MFA (a form of multi-factor authentication), machine learning algorithms and forensic money laundering detection software. By combining historical fraud data, simulated attack scenarios and performance metrics, we assess how these technologies affect the reduction of fraudulent activities using networks as well as their impact on increasing resilience in networks. Results from our study demonstrate that the integration of machine learning algorithms can improve fraudulent detection much more accurately and effective, supported by verification to verify user identities. Furthermore, the real-time anomaly detection systems are capable of sending triggers and responses in response to any sighted potential threats which ultimately make sure that impact caused due fraudulent activities is lowened. The research demonstrates the importance of a layered security posture, deeming it imperative to adapt and improve fraud prevention measures in response to an evolving threat landscape. These findings have important implications for financial organizations that wish to fortify their cybersecurity regimes in this new age of sophisticated online fraud.

Our previous study showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) can learn a behavior as they avoid stimulation from outside. In a sense, the embodied neural network can autonomously change their activity to avoid external stimuli. In this paper, as a result of our experiments using cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: if the network cannot learn to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes as if to ignore the input neurons. We also show such a behavior is reproduced by spiking neural networks with asymmetric-STDP. We consider that these properties can be regarded as autonomous regulation of self and non-self for the network.

The metaverse is a hypothetical iteration that
combines physical reality with virtual reality. Companies have
begun investing in the metaverse, concurrently with a growing
concern for a secure metaverse. In this paper, we delve into the
concepts of cybersecurity theft, the challenges associated with
the integration of AI and the metaverse, and the potential
vulnerabilities that could compromise consumer safety and
privacy. Keywords—component, formatting, style, styling,
insert (key words)

Resumo O presente trabalho apresenta um método baseado em reconhecimento de padrões proteômicos para o diagnóstico precoce do câncer de próstata, utilizando técnicas computacionais, aplicadas na base de dados de padrões proteômicos SELDI-TOF. O método baseia-se em classificar o indivíduo quanto ao estágio de portabilidade do câncer de próstata. Para tanto, usa-se a técnica de Análise de Componentes Independente (ICA) para extrair as características, depois o algoritmo de Máxima Relevância e Mínima Redundância para reduzir o custo computacional, e finalmente a Máquina de Vetores de Suporte para a classificação. Obtêm-se o melhor resultado do método com um vetor de 27 características, alcançando acurácia, especificidade e sensibilidade, respectivamente de 89,21%,

Spectral decomposition has provided a means for observing those features in seismic data that are not always clear in the time domain. There are several approaches that can be used to produce a spectral decomposition of a seismic trace. A case history of using the spectral decomposition and coherency to interpret incised valleys was shown by Peyton et al. (1998). Partyka et al. (1999) used a windowed spectral analysis to produce discretefrequency energy cubes for applications in reservoir characterization. Continuous wavelet

Brain-computer interface (BCI) technology holds promise for revolutionizing interaction between humans and machines, enabling direct communication pathways based on neural signals. However, optimizing BCI applications requires overcoming challenges such as signal noise, latency, and system integration complexities. This paper proposes leveraging Internet of Things (IoT) technologies to enhance BCI applications through real-time data processing, adaptive feedback mechanisms, and seamless connectivity. By integrating IoT with BCI systems, this approach aims to improve usability, reliability, and performance, thereby advancing the capabilities and accessibility of brain-controlled devices in various domains including healthcare, gaming, and assistive technology.

The rapid advancement of technologies enabling human enhancement, from cognitive augmentation to life extension, has sparked significant philosophical debate. However, current posthumanist discourse often focuses on the means of enhancement without a clear vision of its ultimate purpose. This paper introduces the concept of the "Posthuman Polymath," a theoretical framework that positions the infinite pursuit of knowledge and intellectual growth as the primary goal of human enhancement. Drawing from historical examples of polymathy, such as Leonardo da Vinci and Isaac Newton, this framework extends the classical ideal of comprehensive mastery into a posthuman context, where biological limitations no longer constrain intellectual development. Through enhanced cognition, life extension, and artificial intelligence integration, the Posthuman Polymath envisions a future where intellectual achievement can transcend temporal and cognitive boundaries. This paper explores the theoretical foundations, technological pathways, and societal implications of the Posthuman Polymath, arguing that it represents not just an extension of human capabilities, but a fundamental transformation in the nature of knowledge acquisition, consciousness, and human potential. The concept provides a teleological framework for posthumanism, addressing critical gaps in contemporary discourse while offering a practical direction for the development of enhancement technologies.

In the last chapter, Janna van Grunsven, Caroline Bollen and Bouke van Balen show how the phenomenology of communication can inform the field of augmented or alternative communication technology (AAC-tech). AAC-tech is a set of technologies developed for people who are unable to use some of their bodily expressive resources due to congenital or acquired disability. This inability often makes it very difficult for those people to communicate. Developers of AAC-tech often take a cognitivist starting-point, thereby missing out on the subtle ways in which embodiment shapes communication. The phenomenological description of the lived experiences of these people offers a fruitful starting-point for recognizing the often forgotten embodied dimension of communication, and enables to formulate desiderata for how AAC-tech should be developed: AAC-tech should take into account (1) embodied address, (2) embodied enrichment, and (3) embodied diversity. Focusing on the lived experience of potential users of AAC-tech has, according to van Grunsven, Bollen, and van Balen, not only direct practical applications for technology development but also can inform phenomenology methodologically: focusing on a limit case as the one discussed in this chapter makes visible that communication takes place in a wide variety of ways and that it is not the task of the phenomenologist to lay bare a general or essential structure of communication that can be taken as a standard.

Brain Computer Interface (BCI) is a robot/machine communication based on brain activity. The main purpose of BCIs are facilitate the living of disables which has hearing, vision or mobility disabilities. The first and most basic step of the implementation of this system is obtain the recordings of brain signals known as Electroencephalography (EEG). In this study, we aimed to obtain regular EEG signals which were cleaned from negative impacts for BCIs. In this context, analog and digital filter effects were focused to achieve effective and clean EEG signals. Analog and dijital low/high pass filter effects examined on EEG and suitable filter rates are identifyed for clean signals.

Using the GPT algorithm originally developed by OpenAI in February 2019, a skewed or ‘sicko’ AI character was created as a real-time entity running in the Google cloud. The algorithm allows imitations of human dialogue that produce fake and often realistic interactions emanating from computer cloud-based agents. The character was created as one of two characters in the emotionally intelligent artificial intelligence brainwave opera AIBO (Artificial Intelligence Brainwave Opera). The spoken word opera rhetorically inquired “Can an AI be fascist?” and “Can an AI have epigenetic or inherited traumatic memory?” through the interplay of human and non-human characters. This contribution discusses certain aspects involved in creating the GPT cloud-based character AIBO.

AIBO is an embodied, immersive, interactive love story about our infatuation and trust in artificial intelligence, and how AI hierarchies over our experienced emotions. The performance takes place between a human character Eva and AIBO (Artificial Intelligence Brainwave Opera), a custom built ‘sicko’ AI. Eva wears an EEG brainwave headset attached to a body suit of light that displays her emotions as different colors, akin to peeling off her skin to reveal her interior nervous system as light. She performs a spoken word libretto about their love affair. Her brainwaves trigger databanks of videos and audios of her emotionally themed memories. Eva’s libretto, uploaded to the computing cloud is processed by a custom built GPT-2 ‘sicko’ AIBO character, seeded with 47 ‘sicko’ or perverted texts. AIBO’s answers are analyzed by the Natural Language Processing Toolkit in the Google Cloud. The results of these analysis of AIBO’s sentiment emotional values launch different colored backgrounds: green for positive, red for negative and yellow for neutral. AIBO also tries but fails to recreate Eva’s previous emotional memory. It wants to learn how to be human but can only display Eva’s memories as glitchy videos. The opera raises issues about a time when humans and machines potentially merge bodies and consciousness, raising tensions about the embodied vs. the virtual, while also exploring if an AI can be fascist.

In 1875, Richard Caton, an electrophysiologist, discovered that the human brain could create electrical signals and promptly reported his findings to the British Medical Association. In 1929, the German scientist Hans Berger published a report about the alpha signal emitted by the human brain. Originally nicknamed “Berger’s Wave” (now referred to as EEG), it was the first time that the signal had been formally discussed in print. Five years later, Professor (Lord) Adrian and Dr. (Sir Bryan) Matthews demonstrated the actual electrical activity of the human brain during a meeting of the British Physiological Society in Cambridge. This breakthrough revealed and alternatively “performed” (though no one thought of it as such at the time) the brain by using an amplifier and oscillograph placed on a stage. The performance (or experiment), both visual and sonic, was fairly basic: a moving pen, held in place by a mechanical arm, made some wavy lines on a piece of paper. The EEG device was connected by electrode pads to the subject’s scalp, directly above the external occipital lobe. These results were then magnified and displayed onto a projection screen for all to see. The scientist/performer would open and close his eyes or concentrate very intensely while trying to solve a problem like tying and untying a knot. Using graphical recording instruments to display brainwaves was a popular practice at the time, part of a larger program intended to introduce new discoveries that could be adapted for wider public engagement. The
trend originated with the French physiologist Etienne-Jules Marey, who pioneered both motion capture and the use of machines to display circulatory, respiratory, and muscular systems. These events also demonstrate how scientific research into the human brain’s electrical activity was intertwined with live mixed-media performance right from the start.

Since 1972, when Bryan Jennet and Fred Plum coined for the first time the expression "vegetative state", they distinguished "wakefulness" from "awareness", equalizing "consciousness" with observable responses to behavioral stimuli. Recent advances in brain imaging applied to diagnosis of severe brain injury, by enabling us to go beyond observable behavior, are challenging the well-established opinion that vegetative state patients would not be conscious. The moral question of autonomy or of a patient's best interest to continue living or not, indeed, depends on their capacity to realize cost and benefits of their own condition. Regardless of ethical issues about advance directives, withholding or withdrawing nutrition and hydration, the use of functional magnetic resonance imaging brings about bioethical debate to focus on restricted question whether these patients are conscious or not, by diverting our attention from some hidden presuppositions of debate, as equivalence between higher brain and consciousness. Focusing on such equivalence, this paper challenges the socalled "cortico-centric perspective", by arguing that we can detect possible responses to stimuli, not their condition, namely consciousness itself. As subjective experience, indeed, consciousness escapes objective scientific observation, usually being detectable through wider and complicated assessment at the bedside. If consciousness is "the most complex and impenetrable human property" (A. Damasio), the growing use of brain imaging to detect it should be scaled down.

Multielectrode arrays for interfacing with neurons are of great interest for a wide range of medical applications. However, current electrodes cause damage over time. Ultra small carbon fibers help to address issues but controlling the electrode site geometry is difficult. Here we propose a methodology to create small, pointed fiber electrodes (SPFe). We compare the SPFe to previously made blowtorched fibers in characterization. The SPFe result in small site sizes (105.4 ± 20.8 µm 2) with consistently sharp points (20.8 ± 7.64°). Additionally, these electrodes were able to record and/or stimulate neurons multiple animal models including rat cortex, mouse retina, Aplysia ganglia and octopus axial cord. In rat cortex, these electrodes recorded significantly higher peak amplitudes than the traditional blowtorched fibers. These SPFe may be applicable to a wide range of applications requiring a highly specific interface with individual neurons.

The advent of brain-machine interface (BMI) technology has led to a new paradigm in various fields, notably in military technology. BMIs, also known as brain-computer interfaces (BCIs), involve direct communication between the brain and an external device. They interpret brain signals as executable commands, enabling the control of devices through thought. In this article both the terms BMI and BCI have been used interchangeably.