Central neuropathic pain in paraplegia alters movement related potentials

Objectives: Spinal Cord Injured (SCI) persons with and without Central Neuropathic Pain (CNP) show different oscillatory brain activities during imagination of movement. This study investigates whether they also show differences in movement related cortical potentials (MRCP). Methods: SCI paraplegic patients with no CNP (n = 8), with CNP in their lower limbs (n = 8), and healthy control subjects (n = 10) took part in the study. EEG clustering involved independent component analysis, equivalent current dipole fitting, and Measure Projection to define cortical domains that have functional modularity during the motor imagery task. Results: Three domains were identified: limbic system, sensory-motor cortex and visual cortex. The MRCP difference between the groups of SCI with and without CNP was reflected in a domain located in the limbic system, while the difference between SCI patients and control subjects was in the sensorimotor domain. Differences in MRCP morphology between patients and healthy controls were visible for both paralysed and non paralysed limbs. Conclusion: SCI but not CNP affects the movement preparation, and both SCI and CNP affect sensory processes. Significance: Rehabilitation strategies of SCI patients based on MRCP should take into account the presence of CNP

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Applications of Blind Source Separation to the Magnetoencephalogram Background Activity in Alzheimer’s Disease

En esta Tesis Doctoral se ha analizado actividad basal de magnetoencefalograma (MEG) de 36 pacientes con la Enfermedad de Alzheimer (Alzheimer’s Disease, AD) y 26 sujetos de control de edad avanzada con técnicas de separación ciega de fuentes (Blind Source Separation, BSS). El objetivo era aplicar los métodos de BSS para ayudar en el análisis e interpretación de este tipo de actividad cerebral, prestando especial atención a la AD. El término BSS denota un conjunto de técnicas útiles para descomponer registros multicanal en las componentes que los dieron lugar. Cuatro diferentes aplicaciones han sido desarrolladas. Los resultados de esta Tesis Doctoral sugieren la utilidad de la BSS para ayudar en el procesado de la actividad basal de MEG y para identificar y caracterizar la AD.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic

Changes in the EEG Spectrum of a Child with Severe Disabilities in Response to Power Mobility Training

Literature suggests that self-generated locomotion in infancy and early childhood enhances the development of various cognitive processes such as spatial awareness, social interaction, language development and differential attentiveness. Thus, having access to a power mobility device may play a crucial role for the overall development, mental health, and quality of life of children with multiple, severe disabilities who have limited motor control. This study investigates the feasibility of using electroencephalography (EEG) as an objective measure to detect changes in brain activity in a child due to power mobility training. EEG data was collected with a modified wireless neuroheadset using a single-subject A-B-A-B design consisting of two baseline phases (A) and two intervention phases (B). One trial consisted of three different activities during baseline phase; resting condition at the beginning (Resting 1) and at the end (Resting 2) of the trial, interaction with adults, and passive mobility. The intervention phase included a forth activity, the use of power mobility, while power mobility training was performed on another day within the same week of data collection. The EEG spectrum between 2.0 and 12.0 Hz was analyzed for Resting 1 and Resting 2 condition in each phase. We found significant increase of theta power and decrease in alpha power during all three phases following the first baseline. In respect of previous findings, these observations may be related to an increase in alertness and/or anticipation. Analysis of the percentage change from Resting 1 to Resting 2 condition revealed decrease in theta and increasing alpha power during the first intervention phase, which could be associated with increasing cognitive capacity immediately after the use of power mobility. Overall, no significant difference between baseline phase and intervention phase was observed. Thus, whether the observed changes may have been influenced or enhanced by power mobility training remains unclear and warrants further investigation

Effects of probiotics on central nervous system functions in humans

Gut microbiota plays an important role in the gut-brain axis. Symbiosis of the gut microbiota maintains the physiological integrity of the host so as to ensure the normal functions of the gut and the brain. Probiotics have beneficial effects on both, physical and mental health, when administered in adequate amount. Thus, probiotics are considered as “psychobiotics”, for their effects on central nervous system functions such as stress-related mental disorders and memory abilities, through the gut-brain axis. However, the efficacy of the probiotics on these central functions was in need to be systematically summarized. While there is a host of animal studies on microbiota, it has not yet been studied much how and where in the brain of humans they unfold their effects. Furthermore, antibiotics, having effects on commensal gut bacteria by eliminating and inhibiting them, have so far not been studied for their role in affecting brain functions. In the current thesis, I performed two literature reviews and two experimental studies on central effects of pro- and antibiotics. The first review systematically analyzed previous research studying the effects of probiotics on central nervous system functions in both, animals and humans. The review concluded the most efficient probiotic interventions and evaluated the possibility of translating preclinical studies to clinical trials. In the second review, we aimed to evaluate the feasibility of a socio-psychological paradigm (Cyberball game) to be used in the following experimental studies with neuroimaging methods and manipulations of the GM. We examined the current neuroimaging literature employing the Cyberball game to induce social stress and feelings of exclusion. The review was intended to generate a framework describing neural processes during the stress. Following the results of the two reviews, we conducted two clinical trials, to investigate effects of antibiotic rifaximin and probiotic Bifidobacterium longum 1714 on neural activations during resting state and during the Cyberball game by using magnetoencephalography. In both studies, the stress induced by the Cyberball game enhanced oscillatory brain activity in different areas and in different frequency bands. Both, rifaximin and probiotics had effects on specific neural oscillatory activities in response to the social stress – rifaximin improved subjects’ relaxation status by reducing frontal and cingulate beta-1 band power, and B.longum 1714 enhanced emotion regulation process by increasing frontal and cingulate theta and alpha bands power. In addition, during the resting state, rifaximin favored individuals’ relaxation status by increasing frontal alpha band power, and B.longum 1714 increased subject’s arousal state by increasing theta band power in frontal and cingulate cortex and reducing the beta-3 band power in hippocampus and temporal cortex. Rifaximin and B.longum 1714, both showed neural effects on the stress response through an “eubiotic” effect, which refers to a healthy balance of the micro-flora in the gastrointestinal tract. Our results provide evidence for gut microbiota alerting CNS functions. Both, reviews and experimental work give clues for further studies targeting the underlying mechanisms of interaction between gut microbiota and CNS function using neuroimaging in patients with psychiatric disorders or gastrointestinal diseases

Concurrent fNIRS and EEG for brain function investigation: A systematic, methodology-focused review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research

Systems engineering approaches to safety in transport systems

openDuring driving, driver behavior monitoring may provide useful information to prevent road traffic accidents caused by driver distraction. It has been shown that 90% of road traffic accidents are due to human error and in 75% of these cases human error is the only cause. Car manufacturers have been interested in driver monitoring research for several years, aiming to enhance the general knowledge of driver behavior and to evaluate the functional state as it may drastically influence driving safety by distraction, fatigue, mental workload and attention. Fatigue and sleepiness at the wheel are well known risk factors for traffic accidents. The Human Factor (HF) plays a fundamental role in modern transport systems. Drivers and transport operators control a vehicle towards its destination in according to their own sense, physical condition, experience and ability, and safety strongly relies on the HF which has to take the right decisions. On the other hand, we are experiencing a gradual shift towards increasingly autonomous vehicles where HF still constitutes an important component, but may in fact become the "weakest link of the chain", requiring strong and effective training feedback. The studies that investigate the possibility to use biometrical or biophysical signals as data sources to evaluate the interaction between human brain activity and an electronic machine relate to the Human Machine Interface (HMI) framework. The HMI can acquire human signals to analyse the specific embedded structures and recognize the behavior of the subject during his/her interaction with the machine or with virtual interfaces as PCs or other communication systems. Based on my previous experience related to planning and monitoring of hazardous material transport, this work aims to create control models focused on driver behavior and changes of his/her physiological parameters. Three case studies have been considered using the interaction between an EEG system and external device, such as driving simulators or electronical components. A case study relates to the detection of the driver's behavior during a test driver. Another case study relates to the detection of driver's arm movements according to the data from the EEG during a driver test. The third case is the setting up of a Brain Computer Interface (BCI) model able to detect head movements in human participants by EEG signal and to control an electronic component according to the electrical brain activity due to head turning movements. Some videos showing the experimental results are available at https://www.youtube.com/channel/UCj55jjBwMTptBd2wcQMT2tg.openXXXIV CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - Ingegneria dei sistemiZero, Enric