Brain Computer Interaction

Everything that we take for granted in today's scientific practice and various fields of activity is precisely what holds us back from our next cultural and evolutionary leap. This mirrors what occurred during the Renaissance, when the realized need to return to the ancients and their then-neglected cultural heritage revived a vision of being where humanity rediscovered its place in the world. This renewed sensitivity ignited a series of cultural and scientific revolutions that forged the world as we know it. Across global cultures, visual arts that evoke the Renaissance remain highly sought after and revered. The core of that immense revolution sprang from the reinterpretation of ancient classics that flourished in Italy-specifically in Florence and Naples-and was deeply palpable in the philosophy of Ficino and Vico. Its foundational themes encompass the divine, the soul, and the world, alongside aesthetics, imagination, and representation. The way Renaissance thinkers reimagined their world is precisely how we must imagine ours, for it actively shapes reality. The Cybernetic Shaping methodology, developed by the systemic engineering and deep-tech ecosystem and detailed on Aedys, serves as the ideal operational bridge to translate the humanistic ideal of the Renaissance into a cutting-edge scientific methodology. While the Renaissance reimagined humanity's place in the world through philosophy and art, Cybernetic Shaping propels this vision into its next evolutionary leap: it does not merely describe reality, but actively modulates the interaction between biology, energy, and matter to co-design integrated ecosystems rooted both in classical-Renaissance traditions and the futuristic technological vision of cybernetic energy-shaping itself.

Background: The Default Mode Network (DMN) and Frontoparietal Network (FPN) represent two antagonistic large-scale brain systems whose dynamic interplay governs the balance between selfreferential cognition and goal-directed executive control. Therapeutic interventions that modulate the coupling between these networks hold significant promise for cognitive enhancement, attentional disorders, and affective regulation. While non-invasive brain stimulation techniques have advanced rapidly, few integrated software platforms exist that combine multi-sensory entrainment with real-time physiological feedback and cognitive intervention in a single accessible tool. successful induction of subjective cognitive state transitions consistent with enhanced executive function and reduced mind-wandering. The adaptive feedback architecture demonstrates stable real-time performance within the temporal constraints required for effective neural entrainment. Conclusions: This work demonstrates the feasibility of implementing a comprehensive, multi-modal neurocognitive intervention tool as an accessible desktop application. The integration of entrainment science, adaptive physiological feedback, and auto-suggestive techniques within a single platform represents a novel contribution to the field of digital neurotechnology and consumer brain-computer interaction.

La construccion de la memoria es el tema que convoca en las ciencias sociales a los mas diversos especialistas y su relacion con el museo, en el estudio de una exposicion museografica, es lo que desarrolla el presente trabajo. “?Pero ?quien se pre-ocupa de la memoria y por que se pre-ocupa?” (Kingman y Salgado, 2000, p. 125). El tema de la memoria esta siempre vinculado a intereses: la mayoria de autores coinciden en resaltar que implica el olvido. Por tanto, hay que destacar en relacion a la memoria un constante juego de mostrar y ocultar, registrar y borrar. ?Que pretendemos entonces con un trabajo sobre memoria y museo, que analiza una exposicion museografica especifica? La respuesta es introducirnos en la oposicion permanente: revelar / esconder, soslayar / evidenciar.

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.

The development of Brain-Computer Interfaces (BCIs) requires specialists in various fields, including engineering, computer science, medicine and neuroscience. Each of these disciplines possesses a specific and sometimes differing terminology, which creates obstacles to mutual understanding and research collaboration. The IEEE P2731 working group aims to improve communication among BCI researchers by developing a functional model and standard glossary that can be used in all relevant fields. This article describes the anatomical regions of the brain and physiological processes commonly used by BCI applications. It serves as an introduction to neurophysiology for engineers and other non-specialists, and it offers background to articles on the IEEE P2731 Functional Model and Glossary available elsewhere in this special issue.

Tremor is the most common movement disorder, and one of the major causes of functional disability. In spite of the existence of various treatments, tremor is not managed effectively in a large number of patients, which make it a major cause of loss of quality of life. Here we present a novel strategy for tremor suppression through neurostimulation that replicates an adaptive notch-filter at the frequency of the tremor. The controller, which adapts the neurostimulation to the ongoing amplitude, frequency and phase of the tremor, is implemented in a wearable neuroprosthesis for tremor management. Experimental results in one patient with severe essential tremor illustrate the interest of the approach.

The dynamic P300-based speller adjusts the number of flashes per character according to the character's probability of occurrence, as predicted by a language model. The speller consists of two modules: the modified P300 speller using a row-column paradigm, and the prediction by partial matching (PPM) language module. Two cases are considered, prediction hit and prediction miss, according to whether the character predicted by the model coincides with the character intended by the subject. Preliminary experimental results point to the possible advantages of the modified P300 speller which reduces total flash time, while preserving performance.

and Tivnan (2012) recently suggested the existence of a phase transition in the dynamics of financial markets in which there is free interaction between human traders and algorithmic trading systems ('robots'). Above a particular time-threshold, humans and robots trade with one another; below the threshold all transactions are robot-to-robot. We refer to this abrupt system transition as the 'robot phase transition'. Here, we conduct controlled experiments where human traders interact with 'robot' trading agents in minimal models of electronic financial markets to see if correlates of the two regimes suggested by Johnson et al. (2012) occur in such laboratory conditions. Our results indicate that when trading robots act on a superhuman timescale, the market starts to fragment, with statistically lower human-robot interactions than we would expect from a fully mixed market. We tentatively conclude that this is the first empirical evidence for the robot phase transition occurring under controlled experimental conditions.

Softening neural implants that change their elastic modulus under physiological conditions are promising candidates to mitigate neuroinflammatory response due to the reduced mechanical mismatch between the artificial interface and the brain tissue. Intracortical neural probes have been used to demonstrate the viability of this material engineering approach. In our paper, we present a robust technology of softening neural microelectrode and demonstrate its recording performance in the hippocampus of rat subjects. The 5 mm long, single shank, multi-channel probes are composed of a custom thiol-ene/acrylate thermoset polymer substrate, and were micromachined by standard MEMS processes. A special packaging technique is also developed, which guarantees the stable functionality and longevity of the device, which were tested under in vitro conditions prior to animal studies. The 60 micron thick device was successfully implanted to 4.5 mm deep in the hippocampus without the aid of any inser...

The vast majority of P300-based brain-computer interface (BCI) systems are based on the well-known P300 speller presented by Farwell and Donchin for communication purposes and an alternative to people with neuromuscular disabilities, such as impaired eye movement. The purpose of the present work is to study the effect of speller size on P300-based BCI usability, measured in terms of effectiveness, efficiency, and satisfaction under overt and covert attention conditions. To this end, twelve participants used three speller sizes under both attentional conditions to spell 12 symbols. The results indicated that the speller size had, in both attentional conditions, a significant influence on performance. In both conditions (covert and overt), the best performances were obtained with the small and medium speller sizes, both being the most effective. The speller size did not significantly affect workload on the three speller sizes. In contrast, covert attention condition produced very high...

Introduction to the Special Section Drawing a Distinction-Indicating one Sideand Then Crossing the Line The discussion in this special section has been stimulated by the National Research Council report: "Science and Decisions. Advancing Risk Assessment" [1]. In this title, two different domains are simultaneously linked and held apart by the word "and". This word reveals the problem addressed in this special section. Science is comfortable in its own self-referential domain of theory-driven observation, methodical data generation, and hypothetical, i.e. provisional, knowledge production. Science's world cannot be sustained without proper alimentation; hence, there is a lot of pressure to deliver "useful knowledge," which is defined as the kind of knowledge that stimulates economic growth. Natural sciences and engineering sciences claim their advantage by providing results with practical functions, or at least results that may lead to functioning technological realities in the near future. Nowadays, even philosophical research is expected to prove its societal impact and usefulness by contributing to more customer-oriented, sustainable, and ethically defendable technological systems [2]. The crossing from "science" to "decisions" is unavoidable and mandatory, so it appears, but the interaction between science and decision-making is not straightforward. The regulator faces a "dilemma" [3]: On the one hand, decision-makers seem to be asking questions that science cannot give clear answers to (e.g., what are the environmental implications of nanotechnology?) under increasing public pressure. On the other hand, scientists provide answers, in ever greater detail, to questions that decision-makers did not ask or are not interested in (e.g., by what mechanisms do carbon nanotubes cause asbestos-like pathogenicity). Science does not deliver certainty, as the discussions about ignorance or non-knowledge illustrate: "Sometimes, to learn more is to discover hidden complexities that make us realize that the mastery we thought we had over phenomena was in part illusory" [4]. The realization of indeterminacies as a universal fact is like an unmovable object which collides with the irresistible force, i.e. the postulate to make decisions: "Only those questions that are in principle undecidable, we can decide" [5]. While we must decide on matters

This study reports the results of a confirmatory experiment testing the hypothesis that it is possible to detect coincidences of a sequence of events (silence-signal) of different length, by analyzing the EEG activity of two human partners spatially separated when one member of the pair receives the stimulation and the second one is connected only mentally with the first. Seven selected participants with a long friendship and a capacity to maintain focused mental concentration, were divided into two groups located in two different laboratories approximately 190 km apart. Each participant acted both as a "stimulated" and as a "mentally connected" member of the pair for a total of twenty sessions overall. The offline analysis of EEG activity using a special classification algorithm based on a support vector machine, detected the coincidences in the sequence of events of the stimulation protocol between the EEG activity of the "stimulated" and the "mentally connected" pairs. Furthermore the correlation of the power spectra of the five EEG frequency bands between each of the twenty pairs of data was analyzed using a bootstrap procedure. The overall percentage of coincidences out of 88 events was 78.4% and the statistically significant average correlations between the EEG alpha and gamma bands among the pairs of participants, confirmed the results observed in a pilot study. The examination of potential internal, external and statistical artifacts which might have caused these results, ruled out external and internal artifacts. However, the Open Peer Review Approval Status 1 2 version 3 (revision)

Backgound: The main objective of this exploratory study was a confirmation of the results obtained by Giroldini et al, 2016, relative to the possibility of identifying a long-distance connection between the EEG activities of two totally isolated subjects, one of whom was stimulated with light and sounds. In this new study we have used the method of the steady-state stimulus (visual and auditory) given at the frequencies of 10, 12 and 14 Hz in order to answer the following questions:-What is the relationship between the power of the EEG response in the stimulated partner and that of the other isolated partner?-Is the relationship between the EEG activities of the stimulated and the isolated partner global (i.e., an undifferentiated response), or is it differentiated and thus displays variations depending on the characteristics of the stimulation applied to the stimulated partner? Methods: Five adults chosen for their experience in mind control techniques and their mutual friendships took part in this study. Each participant took turns in being both the stimulated partner and the isolated non-stimulated partner with each of the others, making a total of 20 pair combinations. The stimulated partner received three blocks of 32 visual-auditory stimulations lasting 1 second modulated at 10 Hz, 12 Hz, and 14 Hz respectively, with a constant inter-stimulus interval of 4 seconds. The EEG activity of each pair was recorded at 128 samples/sec over 14 channels and analyzed by measuring traditional steady-state potentials and the Pearson's linear correlation between all possible signal pairs with an innovative algorithm. Results: From the results of twenty pairs of subjects, we found an overall increase in the correlation among the EEG channels of the isolated partners, therefore confirming the previous research. Furthermore, we did not find any correlation between the correlation strength among the EEG channels of the stimulated partner and that observed in the non-stimulated partner, suggesting that this physical characteristic cannot be transferred between isolated partners; but we did find that the correlation among the EEG channels of the isolated distant partners changed not only globally, but also when the frequencies perceived by the stimulated partners were outside the Alpha band, suggesting that this neurophysiological mental connection at a distance may be differentiated.

Introduction 9 1 Introduction 21 Part 2  Approaching RRI Governance 23 2 RRI as a governance paradigm: What is new? 31 3 Framings and frameworks: six grand narratives of de facto rri 39 4 Evolution of a concept: a scientometric analysis of RRI 47 5 Res-AGorA concepts and approach 55 6 The Res-AGorA Co-construction Method 63 Part 3  Empirical programme 65 7 Res-AGorA case studies: drawing transversal lessons 77 Case Study 1  Fracking 85 Case Study 2  Danish priority-setting for strategic research 93 Case Study 3  NanoNextNL 101 Case Study 4  Critical organisations 109 Case Study 5  Synthetic biology 115 8 Monitoring RRI in Europe: approach and key observations 121 9 RRI at European universities 127 10 RRI in European member states: the case of Germany 133 Part 4  Governing towards responsibilisation 135 11 Responsibility Navigator 161 12 Conclusion: making responsibility an institutionalised ambition 169 References 181 Authors 184 The Res-AGorA project consortium 185 Acknowledgement 186 Imprint 4  N a v i g a t i n g t o w a r d s s h a r e d r e s p o n s i b i l i t y i n r e s e a r c h a n d i n n o v a t i o n P r e f a c e  5

Introduction 9 1 Introduction 21 Part 2  Approaching RRI Governance 23 2 RRI as a governance paradigm: What is new? 31 3 Framings and frameworks: six grand narratives of de facto rri 39 4 Evolution of a concept: a scientometric analysis of RRI 47 5 Res-AGorA concepts and approach 55 6 The Res-AGorA Co-construction Method 63 Part 3  Empirical programme 65 7 Res-AGorA case studies: drawing transversal lessons 77 Case Study 1  Fracking 85 Case Study 2  Danish priority-setting for strategic research 93 Case Study 3  NanoNextNL 101 Case Study 4  Critical organisations 109 Case Study 5  Synthetic biology 115 8 Monitoring RRI in Europe: approach and key observations 121 9 RRI at European universities 127 10 RRI in European member states: the case of Germany 133 Part 4  Governing towards responsibilisation 135 11 Responsibility Navigator 161 12 Conclusion: making responsibility an institutionalised ambition 169 References 181 Authors 184 The Res-AGorA project consortium 185 Acknowledgement 186 Imprint 4  N a v i g a t i n g t o w a r d s s h a r e d r e s p o n s i b i l i t y i n r e s e a r c h a n d i n n o v a t i o n P r e f a c e  5

P300-based brain computer interface spellers employ the P300 component, which is derived from scalp measured electroencephalogram (EEG) during the brain's electrical response to a flash denoting an attended target character. The most popular P300 speller, the row column paradigm (RCP), displays characters in a matrix within which rows and columns of characters are flashed eliciting P300 responses when the illuminated row or column contains the attended target character. Despite being a longstanding successful approach, this RCP faces several challenges, including the adjacency and crowding problems. A new P300 speller is introducedthe edges paradigm (EP). Distinct from existing P300 spellers, the EP presents a square adjacent to each column or row in the outer boundary of the matrix. By replacing each flash of a row or column with that square, this EP exhibited attenuated influences of crowding and adjacencyproblems known to perturb the RCP. In the copy-spelling mode, 14 neurologically normal participants demonstrated an improved accuracy of 93.3 ± 2.0% for the EP relative to 81.7 ± 2.8% for the RCP, alongside a faster communication rate. Subjective ratings also indicated that the EP caused significantly less fatigue, while increasing alertness and comfort.

Since the dawn of human evolution, man has been searching and designing aspects to meet up the human needs. After the revolutionary change in Technological Advancement, everything has changed the human concept of modern devices and applications. With the buzz alternating around concepts like AI, Machine Learning, Deep Learning, IoT we are approaching the next level of an era of technology and what was once a fantasy is now actually taking place in human history. This paper is upon the research and investigation of the digitized form of the human brain which is processed and analyzed in terms of algorithms and data analysis with integrated data provided from different aspects of human cognition. Using approaches like data analysis, machine learning, neural networking and human perspective approach the formation of the digitized brain has been demonstrated with its approach, usage, and applicability.

P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies (>70%) to account for error revisions. Error-related potentials (ErrP), which are changes in EEG potentials when a person is aware of or perceives erroneous behaviour or feedback, have been proposed as inputs to drive corrective mechanisms that veto erroneous actions by BCI systems. The goal of this study is to demonstrate that training an additional ErrP classifier for a P300 speller is not necessary, as we hypothesize that error information is encoded in the P300 classifier responses used for character selection. We perform off-line simulations of P300 spelling to compare ErrP and non-ErrP based corrective algorithms. A simple dictionary correction based on string matching and word frequency significantly improved accuracy (35-185%), in contrast to an ErrP-based method that...

Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electroencephalography (EEG) signals from the brain of each user into meaningful instructions. This paper proposes a method that removes the calibration phase, and allows a user to control an agent to solve a sequential task. The proposed method assumes a distribution of possible tasks, and infers the interpretation of EEG signals and the task by selecting the hypothesis which best explains the history of interaction. We introduce a measure of uncertainty on the task and on the EEG signal interpretation to act as an exploratory bonus for a planning strategy. This speeds up learning by guiding the system to regions that better disambiguate among task hypotheses. We report experiments where four users use BCI to control an agent on a virtu...

Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibration procedure allows to map signals to specific meanings. In this paper, we address the problem of simultaneously solving a task under human feedback and learning the associated meanings of the feedback signals. This has important practical application since the user can start controlling a device from scratch, without the need of an expert to define the meaning of signals or carrying out a calibration phase. The paper proposes an algorithm that simultaneously assign meanings to signals while solving a sequential task under the assumption that both, human and machine, share the same a priori on the possible instruction meanings and the possible tasks. Furthermore, we show using synthetic and real EEG data from a brain-computer interface that taking into account the uncertainty of the task and the signal is necessary for the machine to actively plan how to solve the task efficiently.

Softening neural implants that change their elastic modulus under physiological conditions are promising candidates to mitigate neuroinflammatory response due to the reduced mechanical mismatch between the artificial interface and the brain tissue. Intracortical neural probes have been used to demonstrate the viability of this material engineering approach. In our paper, we present a robust technology of softening neural microelectrode and demonstrate its recording performance in the hippocampus of rat subjects. The 5 mm long, single shank, multi-channel probes are composed of a custom thiol-ene/acrylate thermoset polymer substrate, and were micromachined by standard MEMS processes. A special packaging technique is also developed, which guarantees the stable functionality and longevity of the device, which were tested under in vitro conditions prior to animal studies. The 60 micron thick device was successfully implanted to 4.5 mm deep in the hippocampus without the aid of any inser...

AI and robotic technologies attract much hype, including utopian and dystopian future visions of technologically driven provision in the health and care sectors. Based on 30 interviews with scientists, clinicians and other stakeholders in the UK, Europe, USA, Australia, and New Zealand, this paper interrogates how those engaged in developing and using AI and robotic applications in health and care characterize their future promise, potential and challenges. We explore the ways in which these professionals articulate and navigate a range of high and low expectations, and promissory and cautionary future visions, around AI and robotic technologies. We argue that, through these articulations and navigations, they construct their own perceptions of socially and ethically ‘acceptable futures’ framed by an ‘ethics of expectations.’ This imbues the envisioned futures with a normative character, articulated in relation to the present context. We build on existing work in the sociology of expectations, aiming to contribute towards better understanding of how technoscientific expectations are navigated and managed by professionals. This is particularly timely since the COVID-19 pandemic gave further momentum to these technologies.

In this chapter, we will explore the P300 wave of visual evoked potentials (VEP), which has become the most popular form of event-related potentials (ERP) in past few decades, its applications and future advancements in the field of P300-based brain computer interface (BCI). The focus of the chapter will be on different design issues considered so far and important challenges to be considered for designing a new P300-based BCI paradigm. In addition, different applications of P300-based BCI systems will be discussed briefly. Applications of electroencephalography (EEG) or the 'brain signals' are emerging rapidly and new ways have been innovated for communication and fast transfer of data between the brain and these applications. Over the last two decades, BCI has made significant progress and substantial research is going on to communicate with the human brain (Wolpaw et al., 2002). One of the few breakthroughs of BCI is a P300-based BCI speller (Farwell & Donchin, 1988). There have been many research studies based on the original design introduced by

In this paper a new neuroscience technique is applied into Marketing, which is becoming commonly known as the field of Neuromarketing. The aim of this paper is to recognize how brain responds during the visualization of short advertising movies. Using low cost electroencephalography (EEG), brain regions used during the presentation have been studied. We may wonder about how useful it is to use neuroscience knowledge in marketing, what can neuroscience add to marketing, or why use this specific technique. By using discrete techniques over EEG frequency bands of a generated labeled dataset, C4.5 and ANN learning methods have been applied to obtain the score assigned to each ads by the user. This techniques allows to reach more than 82% of accuracy, which is an excellent result taking into account the kind of low-cost EEG sensors used.

P300 speller-based brain-computer interface is a direct communication from human-brain to computer machine without any muscular movements. In conventional P300 speller, a display paradigm is used to present alphanumeric characters to users and a classification system is used to detect the target character from the acquired electroencephalographic signals. In this paper, we present an 8 × 8 matrix consisting of Devanagari characters, digits, and special characters as Devanagari script (DS)-based display paradigm. The larger size of the display paradigm as compared with conventional 6 × 6 English row/column (RC) paradigm, involvement of matras and ardha-aksharas and similar looking characters in DS increase the adjacency problem, crowding effect, fatigue, and task difficulty. This results in deteriorated performance at the classification stage. Binary differential evolution algorithm was employed for optimal channel selection and support vector machine was used to classify target verses non target stimuli for the data set collected from ten healthy subjects using the DS-based paradigm. In order to further improve the system reliability in terms of higher accuracy at word prediction level, this paper proposes a novel spelling correction approach based on weighted edit distance (WED). A custom-built dictionary was incorporated and each misspelled word was replaced by a correct word of minimum WED from it. The proposed work is based on the validation of hypothesis that most of the target-error pairs lie in the same RC. Using the proposed spelling correction approach with optimal channel selection, an average accuracy of 99% was achieved at the word prediction level. The statistical analysis carried out in this paper shows that the proposed WED-based method improves the system reliability by significantly increasing in the accuracy of word prediction. This paper also validates that the proposed method performs better as compared to the conventional edit distance-based spelling correction approach.

A brain-computer interface (BCI) is a system that allows a person to control a computer or other device using only their thoughts. BCIs work by measuring brain activity and translating it into commands or signals that a computer or device can understand and respond to. This is typically done using electroencephalography (EEG) or other brain imaging techniques that measure electrical activity in the brain. In this study, user's attention values, maintenance of attention and course/exam success were measured by a game-based BCI and NeuroSky MindWave device developed. The main purpose of the game is to investigate the relationship between time to focus on the game and the student's course success. In the study performed, both the processing of EEG signals read from the sensor was conducted, and a game-based visual BCI interface called KonsantreOL was developed. With the game-based BCI application developed, the relationship between attention values and course success of the students who played the game was investigated. The application was tested on 44 computer engineering students taking the course of "Programming". The fuzzy logic-based analysis of the measured values was performed.

P300 speller-based brain-computer interface is a direct communication from human-brain to computer machine without any muscular movements. In conventional P300 speller, a display paradigm is used to present alphanumeric characters to users and a classification system is used to detect the target character from the acquired electroencephalographic signals. In this paper, we present an 8 × 8 matrix consisting of Devanagari characters, digits, and special characters as Devanagari script (DS)-based display paradigm. The larger size of the display paradigm as compared with conventional 6 × 6 English row/column (RC) paradigm, involvement of matras and ardha-aksharas and similar looking characters in DS increase the adjacency problem, crowding effect, fatigue, and task difficulty. This results in deteriorated performance at the classification stage. Binary differential evolution algorithm was employed for optimal channel selection and support vector machine was used to classify target verses non target stimuli for the data set collected from ten healthy subjects using the DS-based paradigm. In order to further improve the system reliability in terms of higher accuracy at word prediction level, this paper proposes a novel spelling correction approach based on weighted edit distance (WED). A custom-built dictionary was incorporated and each misspelled word was replaced by a correct word of minimum WED from it. The proposed work is based on the validation of hypothesis that most of the target-error pairs lie in the same RC. Using the proposed spelling correction approach with optimal channel selection, an average accuracy of 99% was achieved at the word prediction level. The statistical analysis carried out in this paper shows that the proposed WED-based method improves the system reliability by significantly increasing in the accuracy of word prediction. This paper also validates that the proposed method performs better as compared to the conventional edit distance-based spelling correction approach.

This is a case study involving empirical assessments of EEG signals collected using a Neuro Scan Electrical Signal Imaging brain recording system with 256 electrodes (ESI-256). Subjects were used in an effort to determine and contrast the mental efforts associated with given tasks involving different human-computer interfaces. This article demonstrates and confirms that indeed a particular frequency band between 8 and 13 Hz referred to as alpha activity can be used to characterize under different events or conditions the mental effort or activation associated with basic sensory experiences and mental tasks. The results obtained in this study support this assertion.

In brain-computer interface (BCI) research, there must be a trade-off between accuracy and speed of the BCI system, especially those based on event-related potentials (ERPs). This paper proposes a novel method which can significantly increase the spelling bit rate while also maintaining the desired accuracy. We provide an adaptive real-time stopping method based on the scores of ensemble support vector machine classifiers. We apply a criteria assessment process on the classifiers' scores to dynamically stop the ERP-evoked paradigms at any flashing sequence. Our experiments were conducted on three different P300-Speller data sets (BCI Competition II, BCI Competition III and Akimpech). Our proposed framework significantly outperformed the related state-of-the-art studies in terms of character output accuracy and elicitation bit rate rise between static and dynamic stopping schemes. We improve the average bit rate by over 80% while perfectly maintaining the best original static accuracy of over 96%.

The objective of this experiment was to determine the best possible input EEG feature for classification of the workload while designing load balancing logic for an automated operator. The input features compared in this study consisted of spectral features of Electroencephalography, objective scoring and subjective scoring. Method utilizes to identify best EEG feature as an input in Neural Network Classifiers for workload classification, to identify channels which could provide classification with the highest accuracy and for identification of EEG feature which could give discrimination among workload level without adding any classifiers. The result had shown Engagement Index is the best feature for neural network classification.

Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks is possible because the signals are predefined or an ad-hoc calibration procedure allows to map signals to specific meanings. In this paper, we address the problem of simultaneously solving a task under human feedback and learning the associated meanings of the feedback signals. This has important practical application since the user can start controlling a device from scratch, without the need of an expert to define the meaning of signals or carrying out a calibration phase. The paper proposes an algorithm that simultaneously assign meanings to signals while solving a sequential task under the assumption that both, human and machine, share the same a priori on the possible instruction meanings and the possible tasks. Furthermore, we show using synthetic and real EEG data from a brain-computer interface that taking into account the uncertainty of the task and the signal is necessary for the machine to actively plan how to solve the task efficiently.

Linear and non-linear classifiers provide different strengths for evaluating neurophysiological sensor data. The dynamic nature and non-linear trends of neurophysiological data would appear suited to the use of a non-linear classifier such as an artificial neural network. A research team explored the application of both classification methods and found that successful classification was achieved through the use of a linear classifier. This paper presents the findings of this research team at Lockheed Martin Advanced Technology Laboratories (LM ATL) during its experiences using both types of classifiers and provides a comparison of the strengths and weaknesses of each approach. Recommendations for future use of each type of classifier within augmented cognition are also presented.

Introduction 9 1 Introduction 21 Part 2  Approaching RRI Governance 23 2 RRI as a governance paradigm: What is new? 31 3 Framings and frameworks: six grand narratives of de facto rri 39 4 Evolution of a concept: a scientometric analysis of RRI 47 5 Res-AGorA concepts and approach 55 6 The Res-AGorA Co-construction Method 63 Part 3  Empirical programme 65 7 Res-AGorA case studies: drawing transversal lessons 77 Case Study 1  Fracking 85 Case Study 2  Danish priority-setting for strategic research 93 Case Study 3  NanoNextNL 101 Case Study 4  Critical organisations 109 Case Study 5  Synthetic biology 115 8 Monitoring RRI in Europe: approach and key observations 121 9 RRI at European universities 127 10 RRI in European member states: the case of Germany 133 Part 4  Governing towards responsibilisation 135 11 Responsibility Navigator 161 12 Conclusion: making responsibility an institutionalised ambition 169 References 181 Authors 184 The Res-AGorA project consortium 185 Acknowledgement 186 Imprint 4  N a v i g a t i n g t o w a r d s s h a r e d r e s p o n s i b i l i t y i n r e s e a r c h a n d i n n o v a t i o n P r e f a c e  5

This research aims to provide an easy way to visualize attention levels of a subject through neural interface device and to demonstrate the potential of a neural interface at low cost. A neuroheadset device can play an important role in measuring a subject's attention and inform the subject about his/her attention levels. For few people, it would be better to measure the attention levels before doing an activity that could end up in bad results due to lack of attention which is a common problem in everything ranging from medical errors during operation to driving errors. In the research the attention level of the brain activity is measured to capture the mood of subjects more specifically which is based on two test cases the first test is carried out with a set of questions which includes logic puzzles and mathematical problems and the second test is with a game to measure subject's attention. The system is organized with a set of assessment measures adopted by covariance matrix and cosine similarity metric. Experimental results demonstrate that our approach provides highly correlation among similar attributes under subjects with identical background.

The brain produces weak electrical signals that can be measured from the skull. Electroencephalography (EEG) is a method that provides monitoring electrical activity of the brain with the electrical methods. Brain Computer Interface (BCI) is a system that converts the electrical signals produced by the brain to the signals that can be interpreted by a computer or an electronic system. In the last decades, brain signals could be measured with systems requiring high costs, but nowadays, many EEG devices are available for personal use. These EEG devices and systems have their signal transformation methods. These EEG devices and their SDKs are available for developers and EEG devices are used for purposes such as playing game, psychological relief, monitoring body by daily users. In this study, 'Flying Brain ' game has been developed as BCI based and development stages have been discussed. NeuroSky Mindwave Mobile has been used as EEG device. The game is controlled by attention and meditation measurements obtained by converting the signals received from the user. In the game, the opportunities consist in case of high attention and meditation that will allow players to gain extra points. Consequently, the BCI game was interpreted in terms of Human-Computer Interaction and improvements can be made for BCI based games were discussed.

Designing 3D User Interfaces (UI) requires adequate evaluation tools to ensure good usability and user experience. While many evaluation tools are already available and widely used, existing approaches generally cannot provide continuous and objective measures of usability qualities during interaction without interrupting the user. In this paper, we propose to use brain (with ElectroEncephaloGraphy) and physiological (ElectroCardioGraphy, Galvanic Skin Response) signals to continuously assess the mental effort made by the user to perform 3D object manipulation tasks. We first show how this mental effort (a.k.a., mental workload) can be estimated from such signals, and then measure it on 8 participants during an actual 3D object manipulation task with an input device known as the CubTile. Our results suggest that monitoring workload enables us to continuously assess the 3DUI and/or interaction technique ease-ofuse. Overall, this suggests that this new measure could become a useful addition to the repertoire of available evaluation tools, enabling a finer grain assessment of the ergonomic qualities of a given 3D user interface.

Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engi...

Neural electrodes hold tremendous potential for improving understanding of brain function and restoring lost neurological functions. Multi-walled carbon nanotube (MWCNT) and dexamethasone (Dex)-doped poly(3,4-ethylenedioxythiophene) (PEDOT) coatings have shown promise to improve chronic neural electrode performance. Here, we employ electrochemical techniques to characterize the coating in vivo. Coated and uncoated electrode arrays were implanted into rat visual cortex and subjected to daily cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) for 11 days. Coated electrodes experienced a significant decrease in 1 kHz impedance within the first two days of implantation followed by an increase between days 4 and 7. Equivalent circuit analysis showed that the impedance increase is the result of surface capacitance reduction, likely due to protein and cellular processes encapsulating the porous coating. Coating's charge storage capacity remained consistently higher than uncoated electrodes, demonstrating its in vivo electrochemical stability. To decouple the PEDOT/MWCNT material property changes from the tissue response, in vitro characterization was conducted by soaking the coated electrodes in PBS for 11 days. Some coated electrodes exhibited steady impedance while

Task engagement is a psychological dimension that describes effortful commitment to task goals. This is a multidimensional concept that combines cognition, motivation and emotion. This dimension may be important for the development of physiological computing systems that use real-time psychophysiology to monitor user state, particularly those systems seeking to optimise performance (e.g. adaptive automation, games, automatic tutoring). Two laboratory-based experiments were conducted to investigate measures of task engagement, based on EEG, pupilometry and blood pressure. The first study exposed participants to increased levels of memory load whereas the second used performance feedback to either engage (success feedback) or disengage (failure feedback) participants. EEG variables, such as frontal theta and asymmetry, were sensitive to disengagement due to cognitive load (experiment 1) whilst changes in systolic blood pressure were sensitive to feedback of task success. Implications for the development of physiological computing systems are discussed.

The application of autonomous systems is on an increase, and there is the need to optimize the fit between humans and these systems. While operators must be aware of the autonomous systems dynamic behaviors, the autonomous systems must in turn base their operations, among other things, on an ongoing knowledge of operators' cognitive state, and its application domain. Psychophysiology allows for the use of physiological measurements to understand an operators behavior by noninvasively recording peripheral and central physiological changes while the operator behaves under controlled conditions. Electroencephalography (EEG) is a psychophysiological technique for studying brain activation. In the present study, EEG task engagement index, defined as the ratio of beta to (alpha + theta), are used as inputs to an artificial neural network (ANN) to allow identification and classification of mental engagement. Six separate feedforward ANN with single hidden layer trained by backpropagation were designed to classify five mental tasks for each of six participants. The average classification accuracy across the six participants was 88.67 %. The results show that differences in cognitive task demand do elicit different degrees of mental engagement, which can be measured through the use of the task engagement index.

Attention is one of the main cognitive skills that is constantly used in everyday life. However, various factors can be diminished and even blocked by various disorders, diseases or behaviors that affect people's performance. To analyze the brain signals, the Neurosky MindWave EEG device is required, this device determines the levels of attention of people when they perform some activity and Arduino for data capture. This article compares data obtained from reading the level of care of people of different ages and sex using the Neurosky and Arduino Uno devices. The results obtained show that women (sex) and adults (age) have greater stability of attention over time, and that men (sex) and youth (age) get to obtain higher levels of attention.

Introduction 9 1 Introduction 21 Part 2  Approaching RRI Governance 23 2 RRI as a governance paradigm: What is new? 31 3 Framings and frameworks: six grand narratives of de facto rri 39 4 Evolution of a concept: a scientometric analysis of RRI 47 5 Res-AGorA concepts and approach 55 6 The Res-AGorA Co-construction Method 63 Part 3  Empirical programme 65 7 Res-AGorA case studies: drawing transversal lessons 77 Case Study 1  Fracking 85 Case Study 2  Danish priority-setting for strategic research 93 Case Study 3  NanoNextNL 101 Case Study 4  Critical organisations 109 Case Study 5  Synthetic biology 115 8 Monitoring RRI in Europe: approach and key observations 121 9 RRI at European universities 127 10 RRI in European member states: the case of Germany 133 Part 4  Governing towards responsibilisation 135 11 Responsibility Navigator 161 12 Conclusion: making responsibility an institutionalised ambition 169 References 181 Authors 184 The Res-AGorA project consortium 185 Acknowledgement 186 Imprint 4  N a v i g a t i n g t o w a r d s s h a r e d r e s p o n s i b i l i t y i n r e s e a r c h a n d i n n o v a t i o n P r e f a c e  5

This report, with dissemination restricted to EC use, builds upon previous deliverables of the TECHNOLIFE project. It contains excerpts from D5. 4.1 and may contain overlapping sections with other deliverables and as such it draws on intellectual contributions from the entire TECHNOLIFE Consortium. The authors only are to be held responsible for any mistake or misrepresentation in this report.