EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features

Riemannian geometry has been successfully used in many brain-computer interface (BCI) classification problems and demonstrated superior performance. In this paper, for the first time, it is applied to BCI regression problems, an important category of BCI applications. More specifically, we propose a new feature extraction approach for Electroencephalogram (EEG) based BCI regression problems: a spatial filter is first used to increase the signal quality of the EEG trials and also to reduce the dimensionality of the covariance matrices, and then Riemannian tangent space features are extracted. We validate the performance of the proposed approach in reaction time estimation from EEG signals measured in a large-scale sustained-attention psychomotor vigilance task, and show that compared with the traditional powerband features, the tangent space features can reduce the root mean square estimation error by 4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291

Development of a Practical Visual-Evoked Potential-Based Brain-Computer Interface

There are many different neuromuscular disorders that disrupt the normal communication pathways between the brain and the rest of the body. These diseases often leave patients in a `locked-in state, rendering them unable to communicate with their environment despite having cognitively normal brain function. Brain-computer interfaces (BCIs) are augmentative communication devices that establish a direct link between the brain and a computer. Visual evoked potential (VEP)- based BCIs, which are dependent upon the use of salient visual stimuli, are amongst the fastest BCIs available and provide the highest communication rates compared to other BCI modalities. However. the majority of research focuses solely on improving the raw BCI performance; thus, most visual BCIs still suffer from a myriad of practical issues that make them impractical for everyday use. The focus of this dissertation is on the development of novel advancements and solutions that increase the practicality of VEP-based BCIs. The presented work shows the results of several studies that relate to characterizing and optimizing visual stimuli. improving ergonomic design. reducing visual irritation, and implementing a practical VEP-based BCI using an extensible software framework and mobile devices platforms

Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation

© 2019 American Congress of Rehabilitation Medicine Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic. Design: Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to 1478. Setting: Tertiary care outpatient rehabilitation center. Participant: A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury Interventions: After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements. Main Outcome Measures: Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure–Self-Report [SCIM-SR]) with and without the BCI-FES. Results: With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects. Conclusions: Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

Monitoring driver’s mental workload for user adaptive aid

Gebruikers-adaptieve machines gebruiken sensortechnieken en computer algoritmes om interne toestanden van de gebruiker af te leiden en beslissingen te nemen op basis van deze informatie. Toekomstige generaties auto’s zouden hiermee in staat kunnen worden gesteld actie te ondernemen indien de rijcapaciteit van de bestuurder suboptimaal wordt tijdens het autorijden, zelfs voordat de rijprestatie noemenswaardig verslechtert. De grote uitdaging van zo’n ondersteuning is dat het automatisch, onmiddellijk en op individueel niveau informatie moet kunnen interpreteren, terwijl dit individu beïnvloed wordt door hetzelfde systeem. Mijn proefschrift was gericht op deze uitdaging, waarbij de focus is gelegd op mentale inspanning.In hoofdstuk drie wordt betoogd dat een betrouwbaar systeem waarschijnlijk meerdere typen informatie nodig heeft om de interne toestand vast te kunnen stellen, zoals rijgedrag, fysiologie, en subjectieve ervaringen. Het belangrijkste inzicht uit hoofdstuk vier is dat gebruikers de ondersteuningsacties van adaptief systeem als waarschuwingssignaal kunnen gebruiken, en daarmee het systeem anders gebruikten dan bedoeld. In hoofdstuk vijf werd gekeken naar de mogelijkheid automatische muziekselectie in te zetten teneinde mentale inspanning te beïnvloeden, maar een direct verband tussen inspanning en muzieksoort werd niet aangetoond. In hoofdstuk zes werd de stap gemaakt naar individuele data-analyses uit hersengolven, met zeer goede classificatie resultaten. Uiteindelijk leidde dit tot een op hersengolven gebaseerde cruise control die tijdens (gesimuleerd) autorijden de mentale inspanning van de bestuurder classificeerde, terwijl de rijprestatie ook gemonitord werd (hoofdstuk zeven). Hieruit bleek dat vervolgonderzoek zich zou moeten richten op het verbeteren van de monitorbetrouwbaarheid door het verlagen van de tijds- en contextafhankelijkheid.User adaptive machines use sensor technology and computer algorithms to infer the user’s internal state and make decisions based on this information. Future cars could use this technology to intervene if the capacity of the driver to drive safely is degraded, even before performance starts to break down. The main challenge for such a support system is that it needs to interpret individual data automatically and immediately, while the individual is influenced by the same system. My thesis aims at this challenge, focussing on mental workload. In chapter three it is argued that a reliable system probably needs multiple types of measures to infer the user’s internal state, such as driving performance, physiology, and subjective experiences. The main result from chapter four is that users may use support actions of an adaptive system as a warning signal, and thereby not use the system as intended by the designers. In chapter five the potential was explored to use automatic music selection to influence mental workload was, but a direct link between mental effort and music type was not confirmed. Individual data analyses from brainwaves were the topic of chapter six, resulting in highly accurate workload classifications. This inspired the development of a performance and brain-based cruise control described in chapter seven. The adaptive performance of this system led to the conclusion that future research should focus on decreasing the context and time dependency of workload monitors for user adaptive systems

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine