Automatic Sleep EEG Pattern Detection

Analýza mozkové aktivity je jednou z klícových vyšetrovacích metod v moderní spánkové medicíne a výzkumu.nalysis of recorded brain activity is one of the main investigation methods in modern sleep medicine and research

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Brain-Computer Interfaces using Machine Learning

This thesis explores machine learning models for the analysis and classification of electroencephalographic (EEG) signals used in Brain-Computer Interface (BCI) systems. The goal is 1) to develop a system that allows users to control home-automation devices using their mind, and 2) to investigate whether it is possible to achieve this, using low-cost EEG equipment. The thesis includes both a theoretical and a practical part. In the theoretical part, we overview the underlying principles of Brain-Computer Interface systems, as well as, different approaches for the interpretation and the classification of brain signals. We also discuss the emergent launch of low-cost EEG equipment on the market and its use beyond clinical research. We then dive into more technical details that involve signal processing and classification of EEG patterns using machine leaning. Purpose of the practical part is to create a brain-computer interface that will be able to control a smart home environment. As a first step, we investigate the generalizability of different classification methods, conducting a preliminary study on two public datasets of brain encephalographic data. The obtained accuracy level of classification on 9 different subjects was similar and, in some cases, superior to the reported state of the art. Having achieved relatively good offline classification results during our study, we move on to the last part, designing and implementing an online BCI system using Python. Our system consists of three modules. The first module communicates with the MUSE (a low-cost EEG device) to acquire the EEG signals in real time, the second module process those signals using machine learning techniques and trains a learning model. The model is used by the third module, that takes control of cloud-based home automation devices. Experiments using the MUSE resulted in significantly lower classification results and revealed the limitations of the low-cost EEG signal acquisition device for online BCIs

Multiattention Adaptation Network for Motor Imagery Recognition

This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61873181 and 61922062Peer reviewedPostprin

Cognitive training optimization with a closed-loop system

Les interfaces cerveau-machine (ICMs) nous offrent un moyen de fermer la boucle entre notre cerveau et le monde de la technologie numérique. Cela ouvre la porte à une pléthore de nouvelles applications où nous utilisons directement le cerveau comme entrée. S’il est facile de voir le potentiel, il est moins facile de trouver la bonne application avec les bons corrélats neuronaux pour construire un tel système en boucle fermée. Ici, nous explorons une tâche de suivi d’objets multiples en 3D, dans un contexte d’entraînement cognitif (3D-MOT). Notre capacité à suivre plusieurs objets dans un environnement dynamique nous permet d’effectuer des tâches quotidiennes telles que conduire, pratiquer des sports d’équipe et marcher dans un centre commercial achalandé. Malgré plus de trois décennies de littérature sur les tâches MOT, les mécanismes neuronaux sous- jacents restent mal compris. Ici, nous avons examiné les corrélats neuronaux via l’électroencéphalographie (EEG) et leurs changements au cours des trois phases d’une tâche de 3D-MOT, à savoir l’identification, le suivi et le rappel. Nous avons observé ce qui semble être un transfert entre l’attention et la de mémoire de travail lors du passage entre le suivi et le rappel. Nos résultats ont révélé une forte inhibition des fréquences delta et thêta de la région frontale lors du suivi, suivie d’une forte (ré)activation de ces mêmes fréquences lors du rappel. Nos résultats ont également montré une activité de retard contralatérale (CDA en anglais), une activité négative soutenue dans l’hémisphère contralatérale aux positions des éléments visuels à suivre. Afin de déterminer si le CDA est un corrélat neuronal robuste pour les tâches de mémoire de travail visuelle, nous avons reproduit huit études liées au CDA avec un ensemble de données EEG accessible au public. Nous avons utilisé les données EEG brutes de ces huit études et les avons analysées avec le même pipeline de base pour extraire le CDA. Nous avons pu reproduire les résultats de chaque étude et montrer qu’avec un pipeline automatisé de base, nous pouvons extraire le CDA. Récemment, l’apprentissage profond (deep learning / DL en anglais) s’est révélé très prometteur pour aider à donner un sens aux signaux EEG en raison de sa capacité à apprendre de bonnes représentations à partir des données brutes. La question à savoir si l’apprentissage profond présente vraiment un avantage par rapport aux approches plus traditionnelles reste une question ouverte. Afin de répondre à cette question, nous avons examiné 154 articles appliquant le DL à l’EEG, publiés entre janvier 2010 et juillet 2018, et couvrant différents domaines d’application tels que l’épilepsie, le sommeil, les interfaces cerveau-machine et la surveillance cognitive et affective. Enfin, nous explorons la possibilité de fermer la boucle et de créer un ICM passif avec une tâche 3D-MOT. Nous classifions l’activité EEG pour prédire si une telle activité se produit pendant la phase de suivi ou de rappel de la tâche 3D-MOT. Nous avons également formé un classificateur pour les essais latéralisés afin de prédire si les cibles étaient présentées dans l’hémichamp gauche ou droit en utilisant l’activité EEG. Pour la classification de phase entre le suivi et le rappel, nous avons obtenu un 80% lors de l’entraînement d’un SVM sur plusieurs sujets en utilisant la puissance des bandes de fréquences thêta et delta des électrodes frontales.Brain-computer interfaces (BCIs) offer us a way to close the loop between our brain and the digital world of technology. It opens the door for a plethora of new applications where we use the brain directly as an input. While it is easy to see the disruptive potential, it is less so easy to find the right application with the right neural correlates to build such closed-loop system. Here we explore closing the loop during a cognitive training 3D multiple object tracking task (3D-MOT). Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on MOT tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at the electroencephalography (EEG) neural correlates and their changes across the three phases of a 3D-MOT task, namely identification, tracking and recall. We observed what seems to be a handoff between focused attention and working memory processes when going from tracking to recall. Our findings revealed a strong inhibition in delta and theta frequencies from the frontal region during tracking, followed by a strong (re)activation of these same frequencies during recall. Our results also showed contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered. In order to investigate if the CDA is a robust neural correlate for visual working memory (VWM) tasks, we reproduced eight CDA-related studies with a publicly accessible EEG dataset. We used the raw EEG data from these eight studies and analysed all of them with the same basic pipeline to extract CDA. We were able to reproduce the results from all the studies and show that with a basic automated EEG pipeline we can extract a clear CDA signal. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. In order to address such question, we reviewed 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. Finally, we explore the potential for closing the loop and creating a passive BCI with a 3D-MOT task. We classify EEG activity to predict if such activity is happening during the tracking or the recall phase of the 3D-MOT task. We also trained a classifier for lateralized trials to predict if the targets were presented on the left or right hemifield using EEG brain activity. For the phase classification between tracking and recall, we obtained 80% accuracy when training a SVM across subjects using the theta and delta frequency band power from the frontal electrodes and 83% accuracy when training within subjects

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems

Brain-Computer Interface (BCI) system provides a channel for the brain to control external devices using electrical activities of the brain without using the peripheral nervous system. These BCI systems are being used in various medical applications, for example controlling a wheelchair and neuroprosthesis devices for the disabled, thereby assisting them in activities of daily living. People suffering from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked in are unable to perform any body movements because of the damage of the peripheral nervous system, but their cognitive function is still intact. BCIs operate external devices by acquiring brain signals and converting them to control commands to operate external devices. Motor-imagery (MI) based BCI systems, in particular, are based on the sensory-motor rhythms which are generated by the imagination of body limbs. These signals can be decoded as control commands in BCI application. Electroencephalogram (EEG) is commonly used for BCI applications because it is non-invasive. The main challenges of decoding the EEG signal are because it is non-stationary and has a low spatial resolution. The common spatial pattern algorithm is considered to be the most effective technique for discrimination of spatial filter but is easily affected by the presence of outliers. Therefore, a robust algorithm is required for extraction of discriminative features from the motor imagery EEG signals. This thesis mainly aims in developing robust spatial filtering criteria which are effective for classification of MI movements. We have proposed two approaches for the robust classification of MI movements. The first approach is for the classification of multiclass MI movements based on the thinICA (Independent Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method. The observed results indicate that these approaches can be a step towards the development of robust feature extraction for MI-based BCI system. The main contribution of the thesis is the second criterion, which is based on Alpha- Beta logarithmic-determinant divergence for the classification of two class MI movements. A detailed study has been done by obtaining a link between the AB log det divergence and CSP criterion. We propose a scaling parameter to enable a similar way for selecting the respective filters like the CSP algorithm. Additionally, the optimization of the gradient of AB log-det divergence for this application was also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence) algorithm is proposed for the discrimination of two class MI movements. The robustness of this algorithm is tested with both the simulated and real data from BCI competition dataset. Finally, the resulting performances of the proposed algorithms have been favorably compared with other existing algorithms

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments