EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

Brain-wave measures of workload in advanced cockpits: The transition of technology from laboratory to cockpit simulator, phase 2

The present Phase 2 small business innovation research study was designed to address issues related to scalp-recorded event-related potential (ERP) indices of mental workload and to transition this technology from the laboratory to cockpit simulator environments for use as a systems engineering tool. The project involved five main tasks: (1) Two laboratory studies confirmed the generality of the ERP indices of workload obtained in the Phase 1 study and revealed two additional ERP components related to workload. (2) A task analysis' of flight scenarios and pilot tasks in the Advanced Concepts Flight Simulator (ACFS) defined cockpit events (i.e., displays, messages, alarms) that would be expected to elicit ERPs related to workload. (3) Software was developed to support ERP data analysis. An existing ARD-proprietary package of ERP data analysis routines was upgraded, new graphics routines were developed to enhance interactive data analysis, and routines were developed to compare alternative single-trial analysis techniques using simulated ERP data. (4) Working in conjunction with NASA Langley research scientists and simulator engineers, preparations were made for an ACFS validation study of ERP measures of workload. (5) A design specification was developed for a general purpose, computerized, workload assessment system that can function in simulators such as the ACFS

The Preparation and Production of Rapid Sequential Aiming Movements in Motor Control.

Three experiments are reported that attempt to further our knowledge of the preparation and production of rapid sequential aiming movements. Specifically, these experiments examined possible reasons why in sequential aiming responses, the second movement segment (MT2) is consistently performed more slowly than first and third movement segments (MT1 and MT3). In the first experiment, participants struck 1, 2, or 3 targets in sequence without the benefit of visual feedback and without time stress to determine if MT2 is slowed due to a visually based on-line trajectory-correction process. The results showed that MT2 was not slower than MT1 under these condition. These findings suggest that either visual feedback or reaction time (RT) signal are partly responsible for slowing MT2 when present. The second experiment was conducted using a different avenue of investigation to address the issue of the use of visual feedback. Participants struck three targets in each of five conditions that differed with respect to the size of the first-target (1.5-10 cm diameter) under simple reaction time (SRT) paradigm. The results revealed that MT2 was not slower than the MT1 under the smallest first-target condition. These findings might suggest that lengthening MT1 allowed participants to correct movement error on-line during that segment. The final experiment investigated the possibility that MT2 is lengthened due to the on-line programming of the remainder of the response. Participants were required to perform three-, four-, five-, and six-segment responses within SRT and self-initiation paradigms. The results showed that under RT stress, MT2 was significantly slower than the other movement segments in all responses. However, in the self-initiation condition, MT2 was not significantly slower than MT1 apart from in the six-segment responses. These results suggest that rapid sequential aiming movements might be controlled by a hierarchically organized program that attempts to produce the response in two phases. The first phase of programming controls the first half of the response while the second half of the response tends to be programmed during the end of the execution of the first half of the response

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

Investigating the role of N-methyl-D-aspartate receptor subunit exchange in visual recognition memory

Visual memory is a complex neurophysiological process wherein visual inputs are transduced and encoded within networks of synapsing neurons. At the core of this system are neurotransmitters and postsynaptic receptors, including N-methyl-D-aspartate (NMDA) receptors, which are necessary for visual memory in mice. Interestingly, with age there is a predictable, visual experience-dependent replacement of NMDA receptor subunit NR2B by a second subunit, NR2A. Both subunits have been implicated in regulating potentiation in the murine primary visual cortex (V1) in response to visual stimulus in other forms of plasticity, such as ocular dominance. The goal of this project was to characterize NMDA receptor subunit composition changes during the acquisition of visual memory and to elucidate the role of NR2B in stimulus-specific response potentiation (SRP) and orientation-selective habituation (OSH), which are electrophysiological and behavioral manifestations of visual memory, respectively. To this end, we measured NR2A and NR2B protein levels via Western blot in mice before and after six days of exposure to a sinusoidal grating stimulus. We also evaluated SRP and OSH in mice in which NR2B was selectively deleted by Cre recombinase or pharmacologically inhibited by either CP-101,606 or Ro 25-6981. Our preliminary findings indicate that NMDA subunit exchange in V1 is minimal during the acquisition of visual memory. We observed that the loss of NR2B does not appear to impact SRP or OSH, suggesting that the subunit does not play a role in visual memory, although these biological effects are obscured by high variance and small sample sizes. Finally, we report that DMSO—used as a pharmacological vehicle—may inhibit the acquisition, but not the consolidation of visual memory. Our work here on characterising NMDA receptor subunit NR2B explores one aspect of the biochemical basis of plasticity in V1 and suggests alternative mechanisms that underlie visual memory that warrant further investigation in order to fully understand learning and memory