Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors. The BCI system has a pipeline of 5 stages- signal acquisition, pre-processing, feature extraction, classification and CUDA inter- facing. It shall aid in serving a prototype for physical movement of neurological patients who are unable to control or operate on their muscular movements. All stages of the pipeline are designed to process bodily actions like eye blinks to command navigation of the robot. This prototype works on features learning and classification centric techniques using support vector machine. The suggested pipeline, ensures successful navigation of a robot in four directions in real time with accuracy of 93 percent

BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces

There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data

Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review

Background: Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. Objectives: The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. Type: This was a systematic review. Literature Survey: Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. Methodology: This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. Synthesis: After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. Conclusions: This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. Level of Evidence: I

Neural correlates of flow, boredom, and anxiety in gaming: An electroencephalogram study

Games are engaging and captivating from a human-computer interaction (HCI) perspective as they can facilitate a highly immersive experience. This research examines the neural correlates of flow, boredom, and anxiety during video gaming. A within-subject experimental study (N = 44) was carried out with the use of electroencephalogram (EEG) to assess the brain activity associated with three states of user experience - flow, boredom, and anxiety - in a controlled gaming environment. A video game, Tetris, was used to induce flow, boredom, and anxiety. A 64 channel EEG headset was used to track changes in activation patterns in the frontal, temporal, parietal, and occipital lobes of the players\u27 brains during the experiment. EEG signals were pre-processed and Fast Fourier Transformation values were extracted and analyzed. The results suggest that the EEG potential in the left frontal lobe is lower in the flow state than in the resting and boredom states. The occipital alpha is lower in the flow state than in the resting state. Similarly, the EEG theta in the left parietal lobe is lower during the flow state than the resting state. However, the EEG theta in the frontal-temporal region of the brain is higher in the flow state than in the anxiety state. The flow state is associated with low cognitive load, presence of attention levels, and loss of self-consciousness when compared to resting and boredom states --Abstract, page iii

Innovative Man Machine Interfaces In Aeronautics

The research activity focused on the study, design and evaluation of innovative human-machine interfaces based on virtual three-dimensional environments. It is based on the brain electrical activities recorded in real time through the electrical impulses emitted by the brain waves of the user. The achieved target is to identify and sort in real time the different brain states and adapt the interface and/or stimuli to the corresponding emotional state of the user. The setup of an experimental facility based on an innovative experimental methodology for “man in the loop" simulation was established. It allowed involving during pilot training in virtually simulated flights, both pilot and flight examiner, in order to compare the subjective evaluations of this latter to the objective measurements of the brain activity of the pilot. This was done recording all the relevant information versus a time-line. Different combinations of emotional intensities obtained, led to an evaluation of the current situational awareness of the user. These results have a great implication in the current training methodology of the pilots, and its use could be extended as a tool that can improve the evaluation of a pilot/crew performance in interacting with the aircraft when performing tasks and procedures, especially in critical situations. This research also resulted in the design of an interface that adapts the control of the machine to the situation awareness of the user. The new concept worked on, aimed at improving the efficiency between a user and the interface, and gaining capacity by reducing the user’s workload and hence improving the system overall safety. This innovative research combining emotions measured through electroencephalography resulted in a human-machine interface that would have three aeronautical related applications: • An evaluation tool during the pilot training; • An input for cockpit environment; • An adaptation tool of the cockpit automation

Implementing a Low Noise, Low Power Portable EEG Sensor System

Electroencephalograms or EEGs are useful tools for monitoring brain activity, but they can also be expensive. The goal of this project is to create a wireless, EEG sensor at a lower than typical cost. By using a Teraohm-Input-Impedance FET Buffer, the cost and quality of the electrodes used can be decreased while still achieving sufficient accuracy due to a superior signal to noise ratio. Our system accurately amplifies and displays brain waves with a bandwidth of .5 to 40 Hz while retaining signal quality and minimizing noise within the system. Benefits of this system include a high level of portability, low cost, and simplicity of use

Effects of Spatial Specific Neurofeedback Training in Anterior Cingulate Cortex

This study examines the efficacy of a recently developed methodology of spatial-specific neurofeedback training in the cognitive division of the anterior cingulate gyrus and describes its relationship with cortical regions known to be involved in executive functions and attentional processes. This study was conducted with eight non-clinical students, four male and four female, with a mean age of twenty-two. Exclusion criteria consisted of prior head trauma, neurological or psychiatric disorders, medications and recent drug or alcohol use. Learning occurred in the ACcd at significant levels over sessions and in the anterior regions that receive projections from the AC. There appears to be a multi-dimensional executive circuit that increases in the same frequency in apparent synchrony with the AC and it may be possible to activate this circuit by training one cortical region using LNFB

The Best Self Visualization Method: Clinical Implications and Physiological Correlates

The focus of this study was to evaluate the psychological and psychosocial factors of a novel and composite meditation–visualization practice known as the best self visualization method (BSM). The researcher adapted and modified the BSM to work within a brief 2-week, two-session intervention period. The aim of the study was to evaluate the effects on psychopathology (GAD, PHQ), resilience (PHQ, GAD, Gratitude, Compassion, DSES, Trust, Personal Growth, Mindfulness, and Social Connection), and bio-physiological changes related to EEG spectral power, coherence, and heart rate variability (BPM-Coherence). A total of 66 participants were randomized into active meditation control (n = 35) and BSM (n = 31) intervention groups. Data analysis revealed significant effects for GAD in favor of BSM psychopathology attenuation (U = 316.5, z = -1.62, p ≤ .05) as well as for the PHQ (U = 321.5, z = -1.54, p = .06). Individual t tests revealed a much more robust effect. There were no significant differences for the other scales measured except for interpersonal trust, which had a stronger effect for the BSM group, t(23) = -1.90, p = .04. For EEG and HRV data, results showed the BSM elicited significant changes in parietal gamma spectral power (F[2, 15] = 6.34, p = .010), parietal alpha blocking (F[1, 15] = 5.14, p = .039), and heart rate coherence achievement (t[28] = 1.97, p = .03), as well as demonstrated a heart rate increase trend. Paradoxically, the BSM’s bio-physiological profile was more “activating” when compared to the control, indicating psychopathology attenuation was not related necessarily to a relaxation response

Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability