Relevant bibliographies by topics / Brain-Computer Interfaces (BCIs)

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  2. Dissertations / Theses
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  5. Conference papers
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Academic literature on the topic 'Brain-Computer Interfaces (BCIs)'

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Published: 1 March 2025

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Journal articles on the topic "Brain-Computer Interfaces (BCIs)"

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Berger, Theodore W. "Brain–Computer Interfaces (BCIs)." Journal of Neuroscience Methods 167, no. 1 (January 2008): 1. http://dx.doi.org/10.1016/j.jneumeth.2007.10.002.

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Tang, Feifang, Feiyang Yan, Yushan Zhong, Jinqian Li, Hui Gong, and Xiangning Li. "Optogenetic Brain–Computer Interfaces." Bioengineering 11, no. 8 (August 12, 2024): 821. http://dx.doi.org/10.3390/bioengineering11080821.

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The brain–computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.
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Nijholt, Anton, and Chang S. Nam. "Arts and Brain-Computer Interfaces (BCIs)." Brain-Computer Interfaces 2, no. 2-3 (April 3, 2015): 57–59. http://dx.doi.org/10.1080/2326263x.2015.1100514.

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Klein, Eran, and C. S. Nam. "Neuroethics and brain-computer interfaces (BCIs)." Brain-Computer Interfaces 3, no. 3 (July 2, 2016): 123–25. http://dx.doi.org/10.1080/2326263x.2016.1210989.

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Ma, Yixin, Anmin Gong, Wenya Nan, Peng Ding, Fan Wang, and Yunfa Fu. "Personalized Brain–Computer Interface and Its Applications." Journal of Personalized Medicine 13, no. 1 (December 26, 2022): 46. http://dx.doi.org/10.3390/jpm13010046.

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Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.
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Colman, Jason, and Paul Gnanayutham. "Accessible Button Interfaces." International Journal of Web-Based Learning and Teaching Technologies 7, no. 4 (October 2012): 40–52. http://dx.doi.org/10.4018/jwltt.2012100104.

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The number of people with brain injuries is increasing, as more people who suffer injuries survive. Some of these patients are aware of their surroundings but almost entirely unable to move or communicate. Brain-Computer Interfaces (BCIs) can enable this group of people to use computers to communicate and carry out simple tasks in a limited manner. BCIs tend to be hard to navigate in a controlled manner, and so the use of “one button” user interfaces is explored. This one button concept can not only be used brain injured personnel with BCIs but by other categories of disabled individuals too with alternative point and click devices. A number of accessible button interfaces are described, some of which have already been implemented by the authors.
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Valeriani, Davide, Caterina Cinel, and Riccardo Poli. "Brain–Computer Interfaces for Human Augmentation." Brain Sciences 9, no. 2 (January 24, 2019): 22. http://dx.doi.org/10.3390/brainsci9020022.

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The field of brain–computer interfaces (BCIs) has grown rapidly in the last few decades, allowing the development of ever faster and more reliable assistive technologies for converting brain activity into control signals for external devices for people with severe disabilities [...]
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Ferreira, Alessandro Luiz Stamatto, Leonardo Cunha de Miranda, Erica Esteves Cunha de Miranda, and Sarah Gomes Sakamoto. "A Survey of Interactive Systems based on Brain-Computer Interfaces." Journal on Interactive Systems 4, no. 1 (August 28, 2013): 1. http://dx.doi.org/10.5753/jis.2013.623.

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Brain-Computer Interface (BCI) enables users to interact with a computer only through their brain biological signals, without the need to use muscles. BCI is an emerging research area but it is still relatively immature. However, it is important to reflect on the different aspects of the Human-Computer Interaction (HCI) area related to BCIs, considering that BCIs will be part of interactive systems in the near future. BCIs most attend not only to handicapped users, but also healthy ones, improving interaction for end-users. Virtual Reality (VR) is also an important part of interactive systems, and combined with BCI could greatly enhance user interactions, improving the user experience by using brain signals as input with immersive environments as output. This paper addresses only noninvasive BCIs, since this kind of capture is the only one to not present risk to human health. As contributions of this work we highlight the survey of interactive systems based on BCIs focusing on HCI and VR applications, and a discussion on challenges and future of this subject matter.
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Mikołajewska, Emilia, and Dariusz Mikołajewski. "Ethical considerations in the use of brain-computer interfaces." Open Medicine 8, no. 6 (December 1, 2013): 720–24. http://dx.doi.org/10.2478/s11536-013-0210-5.

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AbstractNervous system disorders are among the most severe disorders. Significant breakthroughs in contemporary clinical practice may provide brain-computer interfaces (BCIs) and neuroprostheses (NPs). The aim of this article is to investigate the extent to which the ethical considerations in the clinical application of brain-computer interfaces and associated threats are being identified. Ethical considerations and implications may significantly influence further development of BCIs and NPs. Moreover, there is significant public interest in supervising this development. Awareness of BCIs’ and NPs’ threats and limitations allow for wise planning and management in further clinical practice, especially in the area of long-term neurorehabilitation and care.
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Xu, Jiahong. "Optimizing Brain-Computer Interfaces through Spiking Neural Networks and Memristors." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 184–90. http://dx.doi.org/10.54097/yk9r3d87.

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Brain-computer interfaces (BCIs) have emerged as a transformative conduit bridging the human brain's intricate realms and computing systems' capabilities. However, numerous challenges remain in improving BCI accuracy, efficiency, and adaptability. This paper investigates the integration of spiking neural networks (SNNs) and memristors to optimize BCI performance. SNNs offer exceptional potential to enhance BCI accuracy through biomimetic modeling of biological neural networks. By emulating the brain's spatio-temporal signaling patterns, SNNs may significantly improve neural decoding precision. Meanwhile, memristors can simulate synaptic plasticity and potentially enable real-time adaptive learning in BCIs. Preliminary studies demonstrate substantially improved signal processing, feature extraction, and classification capabilities when using SNNs and memristors in BCIs. This neuroinspired integration offers a compelling vision for personalized BCIs that continuously adapt to individual users. However, realizing the full potential relies on addressing lingering technical hurdles as well as emerging ethical considerations around user autonomy, privacy, responsibility, and access. Ultimately, interdisciplinary collaboration remains imperative to harness the promising trajectory of optimized BCIs while navigating the multifaceted challenges ahead.
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Dissertations / Theses on the topic "Brain-Computer Interfaces (BCIs)"

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Botrel, Loic [Verfasser], Andrea [Gutachter] Kübler, and Johannes [Gutachter] Hewig. "Brain-computer interfaces (BCIs) based on sensorimotor rhythms - Evaluating practical interventions to improve their performance and reduce BCI inefficiency / Loic Botrel ; Gutachter: Andrea Kübler, Johannes Hewig." Würzburg : Universität Würzburg, 2018. http://d-nb.info/1168146445/34.

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Yamamoto, Maria Sayu. "Addressing the Large Variability of EEG Data with Riemannian Geometry : Toward Designing Reliable Brain-Computer Interfaces." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG098.

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Les interfaces cerveau-ordinateur (BCI) basées sur la géométrie riemannienne ont gagné en popularité au cours de la dernière décennie, démontrant des améliorations significatives dans divers contextes de classification des BCI. Malgré ces avancées, les systèmes BCI restent insuffisamment fiables pour des applications pratiques. L'une des principales difficultés auxquelles les BCI sont confrontées est la variabilité considérable de l'électroencéphalogramme (EEG). Cette variabilité est censée être encore plus prononcée lorsque les systèmes BCI sont utilisés sur plusieurs jours ou en dehors des environnements de laboratoire contrôlés. Cette thèse aborde la grande variabilité de l'EEG sous divers angles sur la variété riemannienne des matrices symétriques définies positives (SPD). Nos six contributions au total peuvent être divisées en trois catégories. Dans la première section, nous proposons deux approches pour atténuer la variabilité de la distribution des données intra-utilisateur sur une variété SPD. La première contribution est une méthode automatique de détection des valeurs aberrantes basée sur le regroupement spectral des matrices SPD EEG, qui permet de détecter les valeurs aberrantes plus précisément que les méthodes existantes de manière entièrement centrée sur les données. La deuxième contribution propose un modèle de classification qui prend en compte les distributions multimodales des matrices SPD sur un collecteur. Notre classificateur multimodal améliore de manière significative la précision de la classification pour un ensemble de données très variables par rapport à un classificateur unimodal standard. La deuxième section traite de la variabilité inter-utilisateur en proposant deux méthodes de sélection de paramètres personnalisées. La première méthode implique une réduction dimensionnelle pour projeter les matrices SPD dans des sous-espaces de basse dimension plus discriminants entre classes, améliorant significativement la précision de classification par rapport à l'espace dimensionnel original. La deuxième méthode est une approche de sélection de bandes de fréquences et de fenêtres temporelles discriminantes basée sur la distinctivité des classes sur une variété SPD. Notre approche de sélection a considérablement amélioré la précision de la classification par rapport à une référence sans sélection de paramètres personnalisés et à une méthode de sélection conventionnelle bien connue. Dans la section finale, nous nous concentrons sur la conception de caractéristiques de classification moins variables dérivées de mesures neurophysiologiques qui ont été sous-utilisées dans les études BCI. Nous proposons de nouvelles représentations de matrices SPD exploitant des couplages inter-fréquences comme caractéristiques de classification, améliorant significativement la précision par rapport aux représentations SPD riemanniennes conventionnelles. De plus, nous avons exploré l'efficacité de la suppression d'une composante hautement variable du signal neural basée sur la paramétrisation périodique/aperiodique des signaux EEG. Cela pourrait contribuer au développement de stratégies neuroscientifiquement interprétables pour aborder la grande variabilité des EEG/BCI. Nos résultats empiriques issus de ces six contributions ouvrent la voie au développement d'algorithmes qui traitent plus efficacement la variabilité significative de l'EEG, faisant progresser la conception d'applications BCI fiables
Riemannian geometry-based Brain-Computer Interfaces (BCIs) have gained momentum over the last decade, demonstrating significant improvements in various BCI classification contexts. Despite these advancements, BCI systems remain insufficiently reliable for practical applications. One of the obstacles facing BCIs is the considerable variability of electroencephalogram (EEG). This variability is expected to be even more pronounced when BCI systems are used over multiple days or outside controlled laboratory environments. This thesis tackled the large variability of EEG data from a variety of angles on the Riemannian manifold of symmetric positive definite (SPD) matrices. Our six contributions can be divided into three categories. In the first section, we proposed two approaches to mitigate the variability of intra-user data distribution on an SPD manifold. The first contribution is an automatic outlier detection method based on spectral clustering for EEG SPD matrices, which could detect outliers more accurately than existing methods in a fully data-driven manner. The second contribution proposed a classification model that accounts for multimodal distributions of SPD matrices on a manifold. Our classifier significantly improved accuracy for a highly variable dataset compared to a standard unimodal classifier. The second section tackled inter-user variability by proposing two personalized parameters selection methods. The first method involves dimensionality reduction to project SPD matrices into more class-discriminating low-dimensional subspaces, significantly enhancing classification accuracy from the original high-dimensional space. The second method is a discriminative frequency band and time window selection approach based on class distinctiveness on an SPD manifold. Our selection approach substantially improved classification accuracy over both a baseline without personalized parameters selection and a well-known conventional selection method. In the final section, we focused on designing less variable classification features derived from neurophysiological measurements that have been underutilized in BCI studies. We propose novel SPD matrix representations that exploit multiple cross-frequency coupling as classification features, significantly improving classification accuracy over conventional Riemannian SPD representations. Additionally, we explored the effectiveness of removing a highly variable component of neural signal based on periodic/aperiodic parameterization of EEG signals. This could contribute to the development of neuroscientifically interpretable strategies for addressing large variability in EEG/BCI. Our empirical findings from these six contributions collectively pave the way for algorithm developments that more effectively address significant EEG variability, advancing the design of reliable BCI applications
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Mindermann, Björn [Verfasser], Axel [Akademischer Betreuer] Gräser, Axel [Gutachter] Gräser, and Canan [Gutachter] Basar-Eroglu. "Untersuchung eines hybriden Brain-Computer Interfaces (BCIs) zur optimalen Auslegung als Mensch-Maschine-Schnittstelle / Björn Mindermann ; Gutachter: Axel Gräser, Canan Basar-Eroglu ; Betreuer: Axel Gräser." Bremen : Staats- und Universitätsbibliothek Bremen, 2018. http://d-nb.info/1159699917/34.

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Bhalotiya, Anuj Arun. "Brain Computer Interface (BCI) Applications: Privacy Threats and Countermeasures." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984122/.

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In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.
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Petrucci, Maila. "Sistemi Brain Computer Interface: dalla macchina al paziente." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10137/.

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C’è un crescente interesse nella comunità scientifica per l’applicazione delle tecniche della bioingegneria nel campo delle interfacce fra cervello e computer. Questo interesse nasce dal fatto che in Europa ci sono almeno 300.000 persone con paralisi agli arti inferiori, con una età media piuttosto bassa (31 anni), registrandosi circa 5.000 nuovi casi ogni anno, in maggioranza dovuti ad incidenti automobilistici. Tali lesioni traumatiche spinali inducono delle disfunzioni sensoriali a causa dell’interruzione tra gli arti e i centri sopraspinali. Per far fronte a questi problemi gli scienziati si sono sempre più proiettati verso un nuovo settore: il Brain Computer Interaction, ossia un ambito della ricerca volto alla costruzione di interfacce in grado di collegare direttamente il cervello umano ad un dispositivo elettrico come un computer.
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Del, Monte Tamara. "Utilizzo dell'elettroencefalografia per la brain-computer interface." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9220/.

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Con Brain-Computer Interface si intende un collegamento diretto tra cervello e macchina, che essa sia un computer o un qualsiasi dispositivo esterno, senza l’utilizzo di muscoli. Grazie a sensori applicati alla cute del cranio i segnali cerebrali del paziente vengono rilevati, elaborati, classificati (per mezzo di un calcolatore) e infine inviati come output a un device esterno. Grazie all'utilizzo delle BCI, persone con gravi disabilità motorie o comunicative (per esempio malati di SLA o persone colpite dalla sindrome del chiavistello) hanno la possibilità di migliorare la propria qualità di vita. L'obiettivo di questa tesi è quello di fornire una panoramica nell'ambito dell'interfaccia cervello-computer, mostrando le tipologie esistenti, cercando di farne un'analisi critica sui pro e i contro di ogni applicazione, ponendo maggior attenzione sull'uso dell’elettroencefalografia come strumento per l’acquisizione dei segnali in ingresso all'interfaccia.
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Jeunet, Camille. "Understanding & Improving Mental-Imagery Based Brain-Computer Interface (Mi-Bci) User-Training : towards A New Generation Of Reliable, Efficient & Accessible Brain- Computer Interfaces." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0221/document.

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Les Interfaces Cerveau-Ordinateur basées sur l’Imagerie Mentale (IM-ICO) permettent auxutilisateurs d’interagir uniquement via leur activité cérébrale, grâce à la réalisation de tâchesd’imagerie mentale. Cette thèse se veut contribuer à l’amélioration des IM-ICO dans le but deles rendre plus utilisables. Les IM-ICO sont extrêmement prometteuses dans de nombreuxdomaines allant de la rééducation post-AVC aux jeux-vidéo. Malheureusement, leurdéveloppement est freiné par le fait que 15 à 30% des utilisateurs seraient incapables de lescontrôler. Nombre de travaux se sont focalisés sur l’amélioration des algorithmes de traitementdu signal. Par contre, l’impact de l’entraînement des utilisateurs sur leur performance estsouvent négligé. Contrôler une IM-ICO nécessite l’acquisition de compétences et donc unentraînement approprié. Or, malgré le fait qu’il ait été suggéré que les protocolesd’entraînement actuels sont théoriquement inappropriés, peu d’efforts sont mis en oeuvre pourles améliorer. Notre principal objectif est de comprendre et améliorer l’apprentissage des IMICO.Ainsi, nous cherchons d’abord à acquérir une meilleure compréhension des processussous-tendant cet apprentissage avant de proposer une amélioration des protocolesd’entraînement afin qu’ils prennent en compte les facteurs cognitifs et psychologiquespertinents et qu’ils respectent les principes issus de l’ingénierie pédagogique. Nous avonsainsi défini 3 axes de recherche visant à investiguer l’impact (1) de facteurs cognitifs, (2) de lapersonnalité et (3) du feedback sur la performance. Pour chacun de ces axes, nous décrivonsd’abord les études nous ayant permis de déterminer les facteurs impactant la performance ;nous présentons ensuite le design et la validation de nouvelles approches d’entraînementavant de proposer des perspectives de travaux futurs. Enfin, nous proposons une solution quipermettrait d’étudier l’apprentissage de manière mutli-factorielle et dynamique : un systèmetutoriel intelligent
Mental-imagery based brain-computer interfaces (MI-BCIs) enable users to interact with theirenvironment using their brain-activity alone, by performing mental-imagery tasks. This thesisaims to contribute to the improvement of MI-BCIs in order to render them more usable. MIBCIsare bringing innovative prospects in many fields, ranging from stroke rehabilitation tovideo games. Unfortunately, most of the promising MI-BCI based applications are not yetavailable on the public market since an estimated 15 to 30% of users seem unable to controlthem. A lot of research has focused on the improvement of signal processing algorithms.However, the potential role of user training in MI-BCI performance seems to be mostlyneglected. Controlling an MI-BCI requires the acquisition of specific skills, and thus anappropriate training procedure. Yet, although current training protocols have been shown tobe theoretically inappropriate, very little research is done towards their improvement. Our mainobject is to understand and improve MI-BCI user-training. Thus, first we aim to acquire a betterunderstanding of the processes underlying MI-BCI user-training. Next, based on thisunderstanding, we aim at improving MI-BCI user-training so that it takes into account therelevant psychological and cognitive factors and complies with the principles of instructionaldesign. Therefore, we defined 3 research axes which consisted in investigating the impact of(1) cognitive factors, (2) personality and (3) feedback on MI-BCI performance. For each axis,we first describe the studies that enabled us to determine which factors impact MI-BCIperformance; second, we describe the design and validation of new training approaches; thethird part is dedicated to future work. Finally, we propose a solution that could enable theinvestigation of MI-BCI user-training using a multifactorial and dynamic approach: an IntelligentTutoring System
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Sicbaldi, Marcello. "Brain-Computer Interface per riabilitazione motoria e cognitiva." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18556/.

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Pazienti con lesioni cerebrali o spinali possono essere affetti da gravi deficit nelle funzioni sensoriali, motorie e comunicative; sono perciò sempre più necessarie tecniche di riabilitazione avanzate, personalizzate e adattative, per limitare i deficit insorti e restituire al paziente una vita il più normale possibile. Negli ultimi decenni, numerosi gruppi di ricerca hanno sviluppato Brain-Computer Interface (BCI) basate sul segnale elettroencefalografico (EEG) con l’obbiettivo di fornire mezzi di comunicazione o riabilitazione motoria funzionale. Tuttavia, le tecnologie BCI hanno un ampio potenziale al di là della sola riabilitazione motoria. Applicazioni dei sistemi BCI in protocolli di riabilitazione cognitiva, ad esempio, hanno conseguito risultati promettenti nella prospettiva di migliorare funzioni quali l’attenzione, l'apprendimento e la memoria in pazienti con disturbi delle funzioni cognitive. In questo lavoro di Tesi si analizzano i principi di funzionamento dei sistemi BCI, a partire dall’acquisizione del segnale elettroencefalografico fino all’estrazione e alla classificazione delle feature del segnale per decodificare intenzioni motorie e processi cognitivi (memoria, attenzione) dell’utente. Viene poi presentata un’analisi della letteratura per quando riguarda gli approcci BCI in riabilitazione sia motoria che cognitiva, prestando particolare attenzione ai metodi utilizzati per l’elaborazione e traduzione del segnale EEG. Sono stati considerati con particolare attenzione studi che valutano gli effetti dell’applicazione di BCI non solo attraverso performance motorie e cognitive ma anche utilizzando tecniche di neuro-imaging avanzate, per indagare possibili cambiamenti nell’organizzazione funzionale della corteccia cerebrale sottostanti i risultati positivi ottenuti. Infine, vengono commentati i vantaggi e le limitazioni di queste tecnologie riabilitative e i problemi ancora aperti.
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JUBIEN, Guillaume. "Decoding Electrocorticography Signals by Deep Learning for Brain-Computer Interface." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243903.

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Brain-Computer Interface (BCI) offers the opportunity to paralyzed patients to control their movements without any neuromuscular activity. Signal processing of neuronal activity enables to decode movement intentions. Ability for patient to control an effector is closely linked to this decoding performance. In this study, I tackle a recent way to decode neuronal activity: Deep learning. The study is based on public data extracted by Schalk et al. for BCI Competition IV. Electrocorticogram (ECoG) data from three epileptic patients were recorded. During the experiment setup, the team asked subjects to move their fingers and recorded finger movements thanks to a data glove. An artificial neural network (ANN) was built based on a common BCI feature extraction pipeline made of successive convolutional layers. This network firstly mimics a spatial filtering with a spatial reduction of sources. Then, it realizes a time-frequency analysis and performs a log power extraction of the band-pass filtered signals. The first investigation was on the optimization of the network. Then, the same architecture was used on each subject and the decoding performances were computed for a 6-class classification. I especially investigated the spatial and temporal filtering. Finally, a preliminary study was conducted on prediction of finger movement. This study demonstrated that deep learning could be an effective way to decode brain signal. For 6-class classification, results stressed similar performances as traditional decoding algorithm. As spatial or temporal weights after training are slightly described in the literature, we especially worked on interpretation of weights after training. The spatial weight study demonstrated that the network is able to select specific ECoG channels notified in the literature as the most informative. Moreover, the network is able to converge to the same spatial solution, independently to the initialization. Finally, a preliminary study was conducted on prediction of movement position and gives encouraging results.
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Bodranghien, Florian. "A novel brain-computer interface (BCI) to assist upper limb pointing movements." Doctoral thesis, Universite Libre de Bruxelles, 2017. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/261534.

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Human to computer interaction only using thoughts is no longer a science fiction topic and recent progress made in this field are astounding. This work shows the creation of a novel upper limb pointing movement performance quantification platform (eCAM test) and its validation on a group of healthy subjects. After that, it shows that functional electrical stimulation (FES) enhances these upper limbs movements performance. Furthermore, this work shows that anodal transcranial direct current stimulation (atDCS) of the cerebellum impacts brain rhythms as well as postural tremor on a patient. Also, the MRI data gathered during this work will allow to better understand the underlying mechanisms of tDCS. Following that, it has been shown that the frequency and complexity of a tapping task increase the postural tremor of the contralateral limb. The same effect has been witnessed for neuromuscular fatigue. All these advances allowed us to place the foundations of a multimodal brain computer interface (BCI) based on sensors fusion. A development phase is now required to create this interface and test it on healthy and sick subjects.
Communiquer avec un ordinateur par le biais de la pensée n'est plus un sujet de science-fiction et les progrès effectués dans le domaine sont ahurissants. Ce travail montre la création d'une nouvelle plateforme de mesure de la performance des mouvements de pointage verticaux (eCAM test) ainsi que sa validation sur une cohorte de sujets sains. Suite à cela, il montre que la stimulation électrique fonctionnelle (FES) améliore la performance de ces mouvements des membres supérieurs. En plus il démontre que la stimulation anodale trancranienne en courant continu (atDCS) du cervelet a un effet sur les rythmes des signaux cérébraux ainsi que sur le tremblement postural d'un patient. De plus des données IRM recueillies durant ce travail permettront de mieux cerner les mécanismes d'action de la stimulation tDCS. Suite à cela, il a été montré que la fréquence et la complexité d'une tâche de tapping augmentent le tremblement postural du membre controlatéral. Le même effet est constaté pour la fatigue musculaire. Toutes ces avancées installent les fondements à la création d'une interface cerveau-machine multimodale basée sur la fusion de senseurs. Une phase de développement est maintenant nécessaire pour établir cette interface et la tester sur des sujets sains et malades.
Doctorat en Sciences biomédicales et pharmaceutiques (Médecine)
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Books on the topic "Brain-Computer Interfaces (BCIs)"

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Pfurtscheller, Gert, Clemens Brunner, and Christa Neuper. EEG-Based Brain–Computer Interfaces. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0047.

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A brain–computer interface (BCI) offers an alternative to natural communication and control by recording brain activity, processing it online, and producing control signals that reflect the user’s intent or the current user state. Therefore, a BCI provides a non-muscular communication channel that can be used to convey messages and commands without any muscle activity. This chapter presents information on the use of different electroencephalographic (EEG) features such as steady-state visual evoked potentials, P300 components, event-related desynchronization, or a combination of different EEG features and other physiological signals for EEG-based BCIs. This chapter also reviews motor imagery as a control strategy, discusses various training paradigms, and highlights the importance of feedback. It also discusses important clinical applications such as spelling systems, neuroprostheses, and rehabilitation after stroke. The chapter concludes with a discussion on different perspectives for the future of BCIs.
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Klein, Eran. Neuromodulation ethics: Preparing for brain–computer interface medicine. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198786832.003.0007.

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Brain–computer interface (BCI) technology is moving from research to clinical practice. Devices that detect seizure patterns and provide preemptive neurostimulation are in clinical use, and significant advancements have been made in BCI-based control of neuroprosthetics and deep brain stimulation systems for treatment of movement disorders. The transition of BCI-based devices into regular clinical use raises ethical challenges for clinicians and patients. Clinicians have important responsibilities in the initial consent process for obtaining BCI devices and in the ongoing management or neuromodulation of patients with BCI-based devices. Rather than understanding neuromodulation as purely technical, it is argued in this chapter that neuromodulation is better thought of as assistive, and that rehabilitation medicine provides a useful framework for beginning to address the kinds of ethical challenges likely to emerge for neuromodulation in BCI medicine.
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Paszkiel, Szczepan, and Wojciech P. Hunek. Biomedical Engineering and Neuroscience: Proceedings of the 3rd International Scientific Conference on Brain-Computer Interfaces, BCI 2018, March ... in Intelligent Systems and Computing). Springer, 2018.

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Book chapters on the topic "Brain-Computer Interfaces (BCIs)"

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Allison, Brendan Z. "Toward Ubiquitous BCIs." In Brain-Computer Interfaces, 357–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02091-9_19.

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Botti Benevides, Alessandro, Mario Sarcinelli-Filho, and Teodiano Freire Bastos-Filho. "Brain–Computer Interfaces (BCIs)." In Introduction to Non-Invasive EEG-Based Brain–Computer Interfaces for Assistive Technologies, 51–60. Boca Raton : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9781003049159-2.

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Gunduz, Aysegul, and Gerwin Schalk. "ECoG-Based BCIs." In Brain–Computer Interfaces Handbook, 297–322. Boca Raton : Taylor & Francis, CRC Press, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351231954-16.

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Flamary, Rémi, Alain Rakotomamonjy, and Michèle Sebag. "Statistical Learning for BCIs." In Brain-Computer Interfaces 1, 185–205. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119144977.ch9.

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Jayaram, Vinay, Karl-Heinz Fiebig, Jan Peters, and Moritz Grosse-Wentrup. "Transfer Learning for BCIs." In Brain–Computer Interfaces Handbook, 425–42. Boca Raton : Taylor & Francis, CRC Press, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351231954-22.

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Taylor, Dawn M. "Functional Electrical Stimulation and Rehabilitation Applications of BCIs." In Brain-Computer Interfaces, 81–94. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8705-9_6.

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Taylor, Dawn M., and Michael E. Stetner. "Intracortical BCIs: A Brief History of Neural Timing." In Brain-Computer Interfaces, 203–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02091-9_12.

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Müller-Putz, Gernot R., Reinhold Scherer, Gert Pfurtscheller, and Rüdiger Rupp. "Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand." In Brain-Computer Interfaces, 171–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02091-9_10.

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Huggins, Jane E. "BCIs Based on Signals from Between the Brain and Skull." In Brain-Computer Interfaces, 221–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02091-9_13.

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Cabestaing, François, and Louis Mayaud. "Medical Applications of BCIs for Patient Communication." In Brain-Computer Interfaces 2, 43–63. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119332428.ch3.

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Conference papers on the topic "Brain-Computer Interfaces (BCIs)"

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Pal, Saptarsi, Shreyansh Mishra, Ajay Kumar, Utkarsh Tiwari, and Mahesh Kumar Singh. "Enhancing Brain Signal Acquisition in Brain-Computer Interfaces (BCIs)." In 2024 2nd International Conference on Disruptive Technologies (ICDT). IEEE, 2024. http://dx.doi.org/10.1109/icdt61202.2024.10489212.

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Wolpaw, Jonathan R. "Brain-computer interfaces (BCIs) for communication and control." In the 9th international ACM SIGACCESS conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1296843.1296845.

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Yakovlev, Lev, Artemiy Berkmush Antipova, Nikolay Syrov, Maksimov Iaroslav, Daria Petrova, Matvey Bulat, Mikhail Lebedev, and Alexander Kaplan. "The effects of tactile stimulation and its imagery on sensorimotor EEG rhythms: incorporating somatic sensations in brain-computer interfaces." In 8th International Conference on Human Interaction and Emerging Technologies. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002765.

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Brain Computer Interface (BCIs) strive to provide a communication channel between the human brain and the environment without using overt actions. To achieve this goal, BCIs convert recordings of neural activity into commands to an external device. One of the most common BCI methods is based on matching of spectral characteristics of EEG sensorimotor rhythms (SMR) to motor imagery attempts. While such motor-imagery BCI have been extensively studied, little is known about the possibility of using tactile imagery as a BCI component. Here we studied EEG modulations associated with tactile imagery and obtained results suggesting that this approach could improve BCI operations.METHODS. 12 healthy naïve volunteers participated in the study. After vibrotactile stimulation was applied several times to the right hand, participants imagined the same sensations in the absence of actual vibration. During this tactile imagery task, 30 channels of EEG were recorded. The effects of tactile imagery were assessed as changes of the SMR in mu and beta bands, which were quantified using EEG desynchronization/synchronization (ERD/S) ratios. An offline classification was conducted for three states: resting, tactile imagery of the right hand, and tactile imagery of the left hand. The classification was based on common spatial pattern (CSP) filtering and linear discriminant analysis.RESULTS. The participants exhibited consistent contralateral ERD patterns over sensorimotor areas during tactile imagery of each hand. Statistically significant differences (p<0.05) in SMR spectral characteristics were found for the comparison of imagery condition to the control state. Offline classification exceeded chance level, as well. CONCLUSION. We found that when human subjects imagine their hands receiving tactile stimulation their SMR spectral characteristics exhibit consistent changes, which could be reliably decoded with a discrete classifier. Based on these observations, we suggest that motor-imagery BCIs could be enriched by adding a tactile-imagery component. Tactile imagery-based BCIs could be especially useful for neuroprosthetic approaches intended for people suffering from somatosensory disabilities and phantom-limb pain.ANCKLOWLEGEMENTS. The study was supported by the Russian Science Foundation grant 21-75-30024.
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Al-Serkal, Abdulla, Nooruldeen Almohammed, Ahmad Qusai, and Jinane Mounsef. "EEG-Based Cognitive Digit Perception for Brain-Computer Interfaces (BCIs)." In 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). IEEE, 2023. http://dx.doi.org/10.1109/gcaiot61060.2023.10385095.

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Сметана, Владимир Васильевич. "BRAIN-COMPUTER INTERFACES (BCI) AND THE PHILOSOPHICAL HORIZONS OF DIGITAL IMMORTALITY." In Перспективные исследования: теория и практика: сборник статей международной научной конференции (Санкт-Петербург, Сентябрь 2024), 27–32. Crossref, 2024. http://dx.doi.org/10.58351/240903.2024.32.84.003.

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Данная научная статья посвящена философскому анализу интерфейсов «мозг-компьютер» (BCI) и их потенциальному влиянию на концепцию цифрового бессмертия. В статье рассматривается концепция и функция BCI как возможного пути к сохранению сознания в цифровой форме, а также текущие достижения в этой области, включая инвазивные и неинвазивные подходы. Особое внимание уделяется потенциалу BCI для записи и интерпретации активности мозга, что открывает перспективы переноса сознания на цифровой субстрат. В статье анализируются философские вопросы, связанные с этой возможностью, включая проблему идентичности цифровой копии сознания, этические аспекты цифрового бессмертия, а также потенциальное влияние на понимание сознания и личности. Кроме того, в статье рассматриваются ограничения и проблемы, связанные с технологией BCI, включая сложность декодирования сигналов мозга, технические ограничения, а также этические проблемы, такие как согласие, приватность и контроль над данными. Цель статьи - стимулировать междисциплинарный диалог и способствовать дальнейшему изучению философских импликаций BCI, включая вопросы о природе сознания, личности, идентичности, этике и будущем человечества в эпоху цифровых технологий. This research paper is devoted to a philosophical analysis of brain-computer interfaces (BCI) and their potential impact on the concept of digital immortality. The article examines the concept and function of BCI as a possible path to digital consciousness preservation, as well as current advances in this field, including invasive and non-invasive approaches. Particular attention is paid to the potential of BCIs to record and interpret brain activity, which opens up the prospect of transferring consciousness to a digital substrate. The article analyzes the philosophical issues surrounding this possibility, including the problem of identity of a digital copy of consciousness, the ethical aspects of digital immortality, and the potential impact on the understanding of consciousness and personality. In addition, the article discusses the limitations and challenges associated with BCI technology, including the difficulty of decoding brain signals, technical limitations, and ethical issues such as consent, privacy, and control over data. The aim of this article is to stimulate interdisciplinary dialogue and further explore the philosophical implications of BCI, including questions about the nature of consciousness, personality, identity, ethics, and the future of humanity in the digital age.
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Heilala, Janne. "Bio-AI Metaverse Integration: Fusion of Surgical and Aerospace Engineering." In Intelligent Human Systems Integration (IHSI 2024) Integrating People and Intelligent Systems. AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004525.

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Recent advancements in real-time brain-computer interfaces (BCIs) hold immense promise for neurosurgery and beyond with a new framework to prepare for backbone surgery. These BCIs can potentially revolutionize surgical precision and patient outcomes by decoding real-time neural signals with 5G. However, their clinical effectiveness is still evolving. This study conducts a comprehensive review and meta-analysis to assess the methodological impact of real-time BCIs in neurosurgery. The research converges two perspectives: one focuses on integrating into humans for neurosurgical enhancements, while the other explores advanced BCIs involving augmented reality and quantum computing from an aerospace human systems integrator viewpoint. This interdisciplinary approach aims to harmoniously integrate these technologies within the human brain, potentially leading to groundbreaking advancements. The methodology employs a pragmatic innovation of cellular composite structured spacecraft with unbent human systems integration.
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Vieites Pérez, Pelayo, Adriana Dapena, and Francisco Laport. "Open Source Simulator of a Control System Based on EEG Signals." In VII Congreso XoveTIC: impulsando el talento científico, 73–80. Servizo de Publicacións. Universidade da Coruña, 2024. https://doi.org/10.17979/spudc.9788497498913.11.

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Brain-computer interfaces (BCIs) are systems that enable communication between a brain and a computer. They have a particularly significant impact on individuals with motor impairments, as they allow users to control devices without physical movement, thereby enhancing their independence and quality of life. Despite their potential, the cost of the devices and licenses required for their development is high. This paper presents the results of a work aimed at developing an open-source BCI simulator that would use the user's eye state to interact with other gadgets. The application eases the testing of various classification algorithms, optimizes hyperparameters and sends messages to external devices. Furthermore, it is a valuable educational tool that can be used in academic settings related to signal processing, machine learning and neural engineering.
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Manuri, Federico, Andrea Sanna, Matteo Bosco, and Francesco De Pace. "A Comparison of Three Different NeuroTag Visualization Media: Brain Visual Stimuli by Monitor, Augmented and Virtual Reality Devices." In 8th International Conference on Human Interaction and Emerging Technologies. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002726.

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Brain Computer Interfaces (BCIs) proved to overcome some limitations of other input modes (e.g., gestures, voice, haptic, etc.). BCIs are able to detect the brain activity, thus identifying searched patterns. When a specific brain activity is detected, a well-defined action can be triggered, thus implementing a human-machine interaction paradigm. BCIs can be used in different domains ranging from industry to services for impaired people.Small and ergonomics devices, such as the NextMind (https://www.next-mind.com/) are the result of recent technological advances; these new devices allow to support users in everyday life, thus bringing the design of BCIs into a new dimension well beyond the scope of laboratory tests.In particular, The NextMind is a device able to detect and classify signals coming from the visual cortex. Visual stimuli are blinking/flickering textures that are associated with objects called NeuroTags (see Figure 1). An event is triggered when the user focuses on the same NeuroTag for a given amount of time. This paradigm can replace selection methods based on keyboard, mouse, gesture, touch, voice, and gaze.This paper compares and assesses three different interfaces that share the same input device (the NextMind) to detect the brain activity and differ in the medium to convey to the user the visual stimuli. A monitor, an Augmented Reality (AR) device (the Microsoft HoloLens), and a Virtual Reality (VR) device (the Oculus Rift) are considered. The aim of this work is to assess any difference in the three visualization media when displaying NeuroTags. User tests have been performed in order to evaluate the usability of the three different solutions. After each test, users were asked for filling out the System Usability Scale (SUS) questionnaire and the SUS scores have been used for statistical analysis.
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Ibarra Chaoul, Andrea, and Moritz Grosse-Wentrup. "Is breathing rate a confounding variable in brain-computer interfaces (BCIs) based on EEG spectral power?" In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7318552.

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Floreani, Erica Danielle, and Tom Chau. "Towards Privacy Preserving BCIs: Profiling the Feasibility of Federated Learning for Motor Imagery Brain-Computer Interfaces." In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10394136.

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Reports on the topic "Brain-Computer Interfaces (BCIs)"

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Potter, Michael, and Lydia Harriss. Brain-computer interfaces. Parliamentary Office of Science and Technology, February 2020. http://dx.doi.org/10.58248/pn614.

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Brain-Computer Interfaces (BCIs) measure brain activity and can be used to control digital devices. The focus of BCI development has been on using the technology to allow patients to control assistive equipment such as wheelchairs or prostheses. Beyond medicine they are under development for applications in entertainment, marketing and defence. This POSTnote looks at the underpinning technology, its applications and the associated ethical and regulatory challenges.
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