Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals

Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to non-sparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels. This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.Comment: Codes are available at: https://sites.google.com/site/researchbyzhang/stsb

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

Brain-Computer Interface-Progress and Prospects

Since the advent of Brain-Computer Interface (BCI), this technology has been significantly contributed modern society in many aspects such as medical and informational science. With further approaches in this interdisciplinary technology and based on current research, BCI is considered to be the potential solution to medical or surgical difficulties such as restoration of neurological function or motor abilities. In this article, the current state of BCI development in multiple platforms was briefly introduced. By organizing and analyzing laboratory data from the state-of-the-art BCI research, this article also illustrated the breakthrough on different BCI systems based on the lab data. The multitude of applications and contributions in medical science and engineering of both invasive and non-invasive systems were also discussed with the help of clinical data. Eventually, the potential and future attempts will be projected and inferred based on the present state of such connection in this article. After comparing and contrasting two types of interfaces and analysis, a conclusion could be made that invasive systems will eventually surpass noninvasive methods in more applications areas due to its preponderance of precise control

DESIGN OF PORTABLE LED VISUAL STIMULUS AND SSVEP ANALYSIS FOR VISUAL FATIGUE REDUCTION AND IMPROVED ACCURACY

Brain-computer interface (BCI) applications have emerged as an innovative communication channel between computers and human brain as it circumvents peripheral limbs thereby creating a direct interface between brain thoughts and the external world. This research focuses on non-invasive BCI to improve the design of visual stimuli in eliciting steady-state visual evoked potential (SSVEP) for BCI applications. To evoke SSVEP in the brain, the user needs to focus on a visual stimulus flickering at a constant frequency. Traditionally in research studies, the visual stimulus for SSVEP uses LCD screens where the flicker is generated using black or white patterns, which alternates the colour to produce a flickering effect. However, there are drawbacks for LCD based visual stimuli systems that limit the user acceptance of SSVEP applications. The main limitations are: (i) choice of flicker frequency is limited to the LCDs vertical refresh rate (ii) flickers are mainly limited to black/white patterns (iii) higher visual fatigue for the user due to LCDs background flicker (iv) reduced visual stimulus portability (v) Inaccurate flickers generated and controlled by the software (vi) influence of adjacent flickers causing attention shift when multiple flickers are used for classification and also not being easily adaptable for user requirements. The impediments in eliciting and utilising SSVEP responses for designing a near real-time platform for controlling external applications are addressed from five main perspectives here: (i) design of standalone LED visual stimulus hardware for precise generation of any frequency for replacing the LCD based visual stimulus (ii) eliciting maximal response by choosing most responsive colour, orientation and shape of visual stimulus (iii) identification of the best luminance level for visual stimulus to improve the comfortability of the user and for improved SSVEP response (iv) control of the duration of ON/OFF period for the visual stimulus to reduce eyestrain for the user (i.e. visual fatigue), and (v) hybrid BCI paradigm using SSVEP and P300 to improve the classification accuracy for controlling external applications. The experimental study involved the development of various visual stimulus designs based on LEDs and microcontrollers to minimise the visual fatigue and improve the SSVEP responses. The signal analysis results from the studies with five to ten participants show SSVEP elicitation is influenced by colour, orientation, the shape of stimulus, the luminance level of stimulus and the duration of ON/OFF period for the stimulus. The participants also commented that choosing the correct luminance and ON/OFF periods of the stimulus considerably reduce the eyestrain, improve the attention levels and reduce the visual fatigue. Taken together, these finding leads to more user acceptance in SSVEP based BCI as an assistive mechanism for controlling external applications with improved comfort, portability and reduced visual fatigue