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

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

Motor Imagery EEG Classification Based on a Weighted Multi-branch Structure Suitable for Multisubject Data

Objective : Electroencephalogram (EEG) signal recognition based on deep learning technology requires the support of sufficient data. However, training data scarcity usually occurs in subject-specific motor imagery tasks unless multisubject data can be used to enlarge training data. Unfortunately, because of the large discrepancies between data distributions from different subjects, model performance could only be improved marginally or even worsened by simply training on multisubject data. Method : This paper proposes a novel weighted multi-branch (WMB) structure for handling multisubject data to solve the problem, in which each branch is responsible for fitting a pair of source-target subject data and adaptive weights are used to integrate all branches or select branches with the largest weights to make the final decision. The proposed WMB structure was applied to six well-known deep learning models (EEGNet, Shallow ConvNet, Deep ConvNet, ResNet, MSFBCNN, and EEG_TCNet) and comprehensive experiments were conducted on EEG datasets BCICIV-2a, BCICIV-2b, high gamma dataset (HGD) and two supplementary datasets. Result : Superior results against the state-of-the-art models have demonstrated the efficacy of the proposed method in subject-specific motor imagery EEG classification. For example, the proposed WMB_EEGNet achieved classification accuracies of 84.14%, 90.23%, and 97.81% on BCICIV-2a, BCICIV-2b and HGD, respectively. Conclusion : It is clear that the proposed WMB structure is capable to make good use of multisubject data with large distribution discrepancies for subject-specific EEG classification

COMPUTATIONAL ANALYSIS OF CODE-MULTIPLEXED COULTER SENSOR SIGNALS

Nowadays, lab-on-a-chip (LoC) technology has been applied in a variety of applications because of its capability to perform accurate microscale manipulations of cells for point-of-care diagnostics. On the other hand, such a result is not readily available from an LoC device and typically still requires a post-inspection of the chip using traditional laboratory equipment such as a microscope, negating the advantages of the LoC technology. To solve this dilemma, my doctoral research mainly focuses on developing portable and disposable biosensors for interfacing with and digitizing the information from an LoC system. Our sensor platform, integrated with multiple microfluidic impedance sensors, electrically monitors and tracks manipulated cells on an LoC device. The sensor platform compresses information from each sensor into a 1-dimensional electrical waveform, and therefore, further signal processing is required to recover the readout of each sensor and extract information of detected cells. Furthermore, with the capability of the sensor platform, we have introduced integrated microfluidic cytometers to characterize properties of cells such as cell surface expression and mechanical properties.Ph.D

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. Charité – Universitätsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. Charité – Universitätsmedizin Berlin; AlemaniaFil: Haufe, Stefan. Charité – Universitätsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. Charité – Universitätsmedizin Berlin; Alemani

Human-robot collaborative task planning using anticipatory brain responses

Human-robot interaction (HRI) describes scenarios in which both human and robot work as partners, sharing the same environment or complementing each other on a joint task. HRI is characterized by the need for high adaptability and flexibility of robotic systems toward their human interaction partners. One of the major challenges in HRI is task planning with dynamic subtask assignment, which is particularly challenging when subtask choices of the human are not readily accessible by the robot. In the present work, we explore the feasibility of using electroencephalogram (EEG) based neuro-cognitive measures for online robot learning of dynamic subtask assignment. To this end, we demonstrate in an experimental human subject study, featuring a joint HRI task with a UR10 robotic manipulator, the presence of EEG measures indicative of a human partner anticipating a takeover situation from human to robot or vice-versa. The present work further proposes a reinforcement learning based algorithm employing these measures as a neuronal feedback signal from the human to the robot for dynamic learning of subtask-assignment. The efficacy of this algorithm is validated in a simulation-based study. The simulation results reveal that even with relatively low decoding accuracies, successful robot learning of subtask-assignment is feasible, with around 80% choice accuracy among four subtasks within 17 minutes of collaboration. The simulation results further reveal that scalability to more subtasks is feasible and mainly accompanied with longer robot learning times. These findings demonstrate the usability of EEG-based neuro-cognitive measures to mediate the complex and largely unsolved problem of human-robot collaborative task planning

Deep Learning for Electrophysiological Investigation and Estimation of Anesthetic-Induced Unconsciousness

Neuroscience has made a number of advances in the search for the neural correlates of consciousness, but our understanding of the neurophysiological markers remains incomplete. In this work, we apply deep learning techniques to resting-state electroencephalographic (EEG) measures of healthy participants under general anesthesia, for the investigation and estimation of altered states of consciousness. Specifically, we focus on states characterized by different levels of unconsciousness and anesthetic depths, based on definitions and metrics from contemporary clinical practice. Our experiments begin by exploring the ability of deep learning to extract relevant electrophysiological features, under a cross-subject decoding task. As there is no state-of-theart model for EEG analysis, we compare two widely used deep learning architectures - convolutional neural networks (cNNs) and multilayer perceptrons (MLPs) - and show that cNNs perform effectively, using only one second of the raw EEG signals. Relying on cNNs, we derive a novel 3D architecture design and a standard preprocessing pipeline, which allows us to exploit the spatio-temporal structure of the EEG, as well as to integrate different acquisition systems and datasets under a common methodology. We then focus on the nature of different predictive tasks, by investigating classification and regression algorithms under a variety of clinical ground-truths, based on behavioral, pharmacological, and psychometrical evidence for consciousness. Our findings provide several insights regarding the interaction across the anesthetic states, the electrophysiological signatures, and the temporal dynamics of the models. We also reveal an optimal training strategy, based on which we can detect progressive changes in levels of unconsciousness, with higher granularity than current clinical methods. Finally, we test the generalizability of our deep learning-based EEG framework, across subjects, experimental designs, and anesthetic agents (propofol, ketamine and xenon). Our results highlight the capacity of our model to acquire appropriate, task-related, cross-study features, and the potential to discover common cross-drug features of unconsciousness. This work has broader significance for discovering generalized electrophysiological markers that index states of consciousness, using a data-driven analysis approach. It also provides a basis for the development of automated, machine-learning driven, non-invasive EEG systems for real-time monitoring of the depth of anesthesia, which can advance patients' comfort and safety