Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

Improved survival for adolescents and young adults with Hodgkin lymphoma and continued high survival for children in the Netherlands:a population-based study during 1990-2015

Population-based studies that assess long-term patterns of incidence, major aspects of treatment and survival are virtually lacking for Hodgkin lymphoma (HL) at a younger age. This study assessed the progress made for young patients with HL (<25 years at diagnosis) in the Netherlands during 1990–2015. Patient and tumour characteristics were extracted from the population-based Netherlands Cancer Registry. Time trends in incidence and mortality rates were evaluated with average annual percentage change (AAPC) analyses. Stage at diagnosis, initial treatments and site of treatment were studied in relation to observed overall survival (OS). A total of 2619 patients with HL were diagnosed between 1990 and 2015. Incidence rates increased for 18–24-year-old patients (AAPC + 1%, P = 0·01) only. Treatment regimens changed into less radiotherapy and more ‘chemotherapy only’, different for age group and stage. Patients aged 15–17 years were increasingly treated at a paediatric oncology centre. The 5-year OS for children was already high in the early 1990s (93%). For patients aged 15–17 and 18–24 years the 5-year OS improved from 84% and 90% in 1990–1994 to 96% and 97% in 2010–2015, respectively. Survival for patients aged 15–17 years was not affected by site of treatment. Our present data demonstrate that significant progress in HL treatment has been made in the Netherlands since 1990

Modulation of Transcriptional and Inflammatory Responses in Murine Macrophages by the Mycobacterium tuberculosis Mammalian Cell Entry (Mce) 1 Complex

The outcome of many infections depends on the initial interactions between agent and host. Aiming at elucidating the effect of the M. tuberculosis Mce1 protein complex on host transcriptional and immunological responses to infection with M. tuberculosis, RNA from murine macrophages at 15, 30, 60 min, 4 and 10 hrs post-infection with M. tuberculosis H37Rv or Δ-mce1 H37Rv was analyzed by whole-genome microarrays and RT-QPCR. Immunological responses were measured using a 23-plex cytokine assay. Compared to uninfected controls, 524 versus 64 genes were up-regulated by 15 min post H37Rv- and Δ-mce1 H37Rv-infection, respectively. By 15 min post-H37Rv infection, a decline of 17 cytokines combined with up-regulation of Ccl24 (26.5-fold), Clec4a2 (23.2-fold) and Pparγ (10.5-fold) indicated an anti-inflammatory response initiated by IL-13. Down-regulation of Il13ra1 combined with up-regulation of Il12b (30.2-fold), suggested switch to a pro-inflammatory response by 4 hrs post H37Rv-infection. Whereas no significant change in cytokine concentration or transcription was observed during the first hour post Δ-mce1 H37Rv-infection, a significant decline of IL-1b, IL-9, IL-13, Eotaxin and GM-CSF combined with increased transcription of Il12b (25.1-fold) and Inb1 (17.9-fold) by 4 hrs, indicated a pro-inflammatory response. The balance between pro-and anti-inflammatory responses during the early stages of infection may have significant bearing on outcome

Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research

Results that do not confirm expectations are generally referred to as ‘negative’ results. While essential for scientific progress, they are too rarely reported in the literature – Brain–Machine Interface (BMI) research is no exception. This led us to organize a workshop on BMI negative results during the 2018 International BCI meeting. The outcomes of this workshop are reported herein. First, we demonstrate why (valid) negative results are useful, and even necessary for BMIs. These results can be used to confirm or disprove current BMI knowledge, or to refine current theories. Second, we provide concrete examples of such useful negative results, including the limits in BMI-control for complete locked-in users and predictors of motor imagery BMI performances. Finally, we suggest levers to promote the diffusion of (valid) BMI negative results, e.g. promoting hypothesis-driven research using valid statistical tools, organizing special issues dedicated to BMI negative results, or convincing institutions and editors that negative results are valuable

Brain-Computer Interface Based on Generation of Visual Images

This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier

An Introduction to EEG Source Analysis with an illustration of a study on Error-Related Potentials

International audienceOver the last twenty years blind source separation (BSS) has become a fundamental signal processing tool in the study of human electroencephalography (EEG), other biological data, as well as in many other signal processing domains such as speech, images, geophysics and wireless communication (Comon and Jutten, 2010). Without relying on head modeling BSS aims at estimating both the waveform and the scalp spatial pattern of the intracranial dipolar current responsible of the observed EEG, increasing the sensitivity and specificity of the signal received from the electrodes on the scalp. This chapter begins with a short review of brain volume conduction theory, demonstrating that BSS modeling is grounded on current physiological knowledge. We then illustrate a general BSS scheme requiring the estimation of second-order statistics (SOS) only. A simple and efficient implementation based on the approximate joint diagonalization of covariance matrices (AJDC) is described. The method operates in the same way in the time or frequency domain (or both at the same time) and is capable of modeling explicitly physiological and experimental source of variations with remarkable flexibility. Finally, we provide a specific example illustrating the analysis of a new experimental study on error-related potentials