The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

Brain-Computer Interfaces

Die vorliegende Dissertation beschreibt Ergebnisse der Arbeit, durchgeführt am Fraunhofer Institut für Rechnerarchitektur und Softwaretechnik (FIRST), insbesondere bei der Forschungsgruppe für Intelligente Datenanalyse (IDA), im Rahmen des Projektes "Brain-Computer Interface" (BCI). Das angestrebte Ziel des aktuell laufenden Projektes ist es, ein Hardware- und Software-System zu entwerfen und zu entwickeln, das in der Lage ist elektroencephalographische (EEG)Signale(gewonnen auf eine nicht-invasive Art, mit Hilfe der Oberflächenelektroden, die über dem Kopf des Benutzers angebracht sind), in Echtzeit in spezielle Kommandos umzuwandeln, so dass für den Probanden eine verlässliche Steuerung einer Computeranwendung, bzw. eines Gerätes ermöglicht wird. Die Steuerung einer Computeranwendung soll im Rahmen dieser Arbeit in Form von einfachen Computerspielen (Ping-Pong, Pacman, Tetris) repräsentiert werden, im Weiteren bezeichnet als "Brain-Gaming". Hierzu sind fundierte Überlegungen zum Entwurf und Realisierung einer Kommunikationsschnittstelle und des zugehörigen Protokolls angestellt worden. Ein weiterer wichtiger Bestandteil jeder Steuerung ist deren Strategie und der Befehlssatz der Anwendung. So wurden mehrere Strategien entwickelt, implementiert und in verschiedenen Szenarien experimentell erprobt. Die Flexibilität der Steuerungsschnittstelle stellte sich als einer der wichtigsten Aspekte beim Entwurf und der Entwicklung von Rückkopplungsanwendungen. Bei der Steuerung eines Gerätes kann es sich um das Lenken und Bewegen eines Rollstuhls, z.B. für querschnittsgelehmte Patienten, oder um das Bewegen einer Arm-, bzw. Beinprothese für Patienten mit amputierten Extremitäten handeln. Dies wurde vorerst als Simulation einer Extremität (Arm) auf dem Computer-Bildschirm realisiert, so dass es in zukünftigen Experimenten an bedürftigen Patienten getestet werden kann. Im Gegensatz zu Brain-Gaming Experimenten, bei denen der Spieler mit einem zusätzlichen Kommunikationskanal (der unabhängig von anderen normalen Kanälen des menschlichen neuromuskulären Systems ist) ausgestattet wird, stellen die Experimente an Patienten keine Anforderung an die ultra-schnelle Erkennung der Bewegungsabsicht; So kann das Steuersignal zur Ausführung einer simulierten Bewegung auch nach dem Auslösen der eigentlichen Phantombewegung, sogar nach deren Ausführung, erkannt werden. In Experimenten mit Feedback-Szenarien, die kompetitiven Spielen ähneln, können verschiedene Aspekte der ultraschnellen Erkennung einer Bewegungsintension mit Hilfe von Reaktionstests untersucht werden. Dieses eröffnet neue Perspektiven bei der Ausführung von Präventivmaßnahmen in zeitkritischen Anwendungen. Diese Dissertation, sowie die Entwicklung und Implementierung des Prototyps basiert auf fundierten Erkenntnissen der Neurophysiologie; Ein ausgewähltes Kapitel dieser Dissertation verschafft deshalb tieferen Einblick in die menschliche Neurophysiologie. Ferner, beschreibt ein gesondertes Kapitel dieser Dissertation die Entwicklung und Implementierung eines online Prototyps " des BBCI-Systems (Berliner Brain-Computer Interface) " in dessen Einzelkomponenten und der Gesamtheit aus dem Sichtpunkt der Softwaretechnik. Im Rahmen der Arbeit wurden mehrere Rückkopplungsmodule (Bio-Feedback), sowohl spielerischen Charakters, als auch zu Rehabilitationszwecken entwickelt, die hier im Detail vorgestellt werden. Mit besonderer Aufmerksamkeit wurde der Einfluss des online Bio-Feedbacks auf den Probanden untersucht.This dissertation aims to describe work carried out at the Fraunhofer Institute for Computer Architecture and Software Technology (FhG-FIRST), in particular at the research group for Intelligent Data Analysis (IDA), within the project "Brain-Computer Interface" (BCI). The goal of that project is to design and develop a hardware and software system that is capable of transforming, in real-time, electroencephalographic (EEG) signals (signals retrieved in a non-invasive way from surface electrodes placed over the user's head) into specific commands such that the user gains reliable control over a computer application or a device. In this dissertation, control over a computer application will be represented with "Brain-Gaming", i.e. simple computer games like Ping-Pong, Pacman or Tetris. To this end, substantial considerations were made on the design and realization of a communication interface and its corresponding protocol. A further important component of every control operation is its strategy and the control alphabet, i.e. the command set. For this purpose, several control strategies were developed, implemented and proved experimentally in different scenarios. The most important aspect of the design and development of these interfaces turned out to be their flexibility. Control over a device could include the steering and moving of a wheel-chair for paralyzed patients or the gaining of control over an arm or foot prosthesis for patients with amputated limbs. The latter was realized here as a computer-based simulation of a virtual limb (e.g. arm), such that it can be tested in future experiments on patients with amputated limbs. In contrast to the "Brain-Gaming" experiments, where the player was equipped with an additional communication channel (one that exists independently of normal communication channels of the human neuromuscular system), the experiments with patients did not require an ultra-fast recognition of the intended movement; i.e. the command signal for a simulated movement can be recognized after the phantom movement is initialized and performed. In experiments with feedback scenarios, which can be resembled as competitive games, several aspects of ultra-fast movement detection could be investigated with reaction tests. This opens new perspectives for the execution of preventive actions in time-critical applications. This dissertation, and the development and implementation of the prototype, is based on well-founded insights into human neurophysiology; one chapter will deal exclusively with these insights. Moreover, a special chapter of this dissertation will also describe the development and implementation of an online prototype of the BBCI system (Berlin Brain-Computer Interface) from the software engineering viewpoint. A number of bio-feedback modules, for gaming and for rehabilitation purposes, were developed within this work and will be presented in detail. Special attention was paid to the influence of the online bio-feedback on the user's behavior

Games and Brain-Computer Interfaces: The State of the Art

BCI gaming is a very young field; most games are proof-of-concepts. Work that compares BCIs in a game environments with traditional BCIs indicates no negative effects, or even a positive effect of the rich visual environments on the performance. The low transfer-rate of current games poses a problem for control of a game. This is often solved by changing the goal of the game. Multi-modal input with BCI forms an promising solution, as does assigning more meaningful functionality to BCI control

BNCI Horizon 2020 - Towards a Roadmap for Brain/Neural Computer Interaction

In this paper, we present BNCI Horizon 2020, an EU Coordination and Support Action (CSA) that will provide a roadmap for brain-computer interaction research for the next years, starting in 2013, and aiming at research efforts until 2020 and beyond. The project is a successor of the earlier EU-funded Future BNCI CSA that started in 2010 and produced a roadmap for a shorter time period. We present how we, a consortium of the main European BCI research groups as well as companies and end user representatives, expect to tackle the problem of designing a roadmap for BCI research. In this paper, we define the field with its recent developments, in particular by considering publications and EU-funded research projects, and we discuss how we plan to involve research groups, companies, and user groups in our effort to pave the way for useful and fruitful EU-funded BCI research for the next ten years

Brain-Driven Representation Learning Based on Diffusion Model

Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. Denoising diffusion probabilistic models (DDPMs), which have recently gained prominence in diverse areas for their capabilities in representation learning, are explored in our research as a means to address this issue. Using DDPMs in conjunction with a conditional autoencoder, our new approach considerably outperforms traditional machine learning algorithms and established baseline models in accuracy. Our results highlight the potential of DDPMs as a sophisticated computational method for the analysis of speech-related EEG signals. This could lead to significant advances in brain-computer interfaces tailored for spoken communication

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices

Prediction of difficulty levels in video games from ongoing EEG

Real-time assessment of mental workload from EEG plays an important role in enhancing symbiotic interaction of human operators in immersive environments. In this study we thus aimed at predicting the difficulty level of a video game a person is playing at a particular moment from the ongoing EEG activity. Therefore, we made use of power modulations in the theta (4–7 Hz) and alpha (8–13 Hz) frequency bands of the EEG which are known to reflect cognitive workload. Since the goal was to predict from multiple difficulty levels, established binary classification approaches are futile. Here, we employ a novel spatial filtering method (SPoC) that finds spatial filters such that their corresponding bandpower dynamics maximally covary with a given target variable, in this case the difficulty level. EEG was recorded from 6 participants playing a modified Tetris game at 10 different difficulty levels. We found that our approach predicted the levels with high accuracy, yielding a mean prediction error of less than one level.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio