Katrien Vanderperren

Removal of BCG artifacts from EEG recordings inside the MR scanner: A comparison of methodological and validation-related aspects

Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent.

Removal of Muscle Artifacts from EEG Recordings of Spoken Language Production

Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.

The "why" and "how" of JointICA: results from a visual detection task

Since several years, neuroscience research started to focus on multimodal approaches. One such multimodal approach is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, no standard integration procedure has been established so far. One promising data-driven approach consists of a joint decomposition of event-related potentials (ERPs) and fMRI maps derived from the response to a particular stimulus. Such an algorithm (joint independent component analysis or JointICA) has recently been proposed by Calhoun et al. (2006). This method provides sources with both a fine spatial and temporal resolution, and has shown to provide meaningful results. However, the algorithms performance has not been fully characterized yet, and no procedure has been proposed to assess the quality of the decomposition. In this paper, we therefore try to answer why and how JointICA works. We show the performance of the algorithm on data obtained in a visual detection task, and compare the performance for EEG recorded simultaneously with fMRI data and for EEG recorded in a separate session (outside the scanner room). We perform several analyses in order to set the necessary conditions that lead to a sound decomposition, and to give additional insights for exploration in future studies. In that respect, we show how the algorithm behaves when different EEG electrodes are used and we test the robustness with respect to the number of subjects in the study. The performance of the algorithm in all the experiments is validated based on results from previous studies.

The dynamics of contour integration: A simultaneous EEG-fMRI study

To study the dynamics of contour integration in the human brain, we simultaneously acquired EEG and fMRI data while participants were engaged in a passive viewing task. The stimuli were Gabor arrays with some Gabor elements positioned on the contour of an embedded shape, in three conditions: with local and global structure (perfect contour alignment), with global structure only (orthogonal orientations interrupting the alignment), or without contour. By applying JointICA to the EEG and fMRI responses of the subjects, new insights could be obtained that cannot be derived from unimodal recordings. In particular, only in the global structure condition, an ERP peak around 300ms was identified that involved a loop from LOC to the early visual areas. This component can be interpreted as being related to the verification of the consistency of the different local elements with the globally defined shape, which is necessary when perfect local-to-global alignment is absent. By modifying JointICA, a quantitative comparison of brain regions and the time-course of their interplay were obtained between different conditions. More generally, we provide additional support for the presence of feedback loops from higher areas to lower level sensory regions.

Single trial ERP reading based on parallel factor analysis

The extraction of task-related single trial ERP features has recently gained much interest, in particular in simultaneous EEG-fMRI applications. In this study, a specific decomposition known as parallel factor analysis (PARAFAC) was used, in order to retrieve the task-related activity from the raw signals. Using visual detection task data, acquired in normal circumstances and simultaneously with fMRI, differences between distinct task-related conditions can be captured in the trial signatures of specific PARAFAC components when applied to ERP data arranged in Channels × Time × Trials arrays, but the signatures did not correlate with the fMRI data. Despite the need for parameter tuning and careful preprocessing, the approach is shown to be successful, especially when prior knowledge about the expected ERPs is incorporated.

Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration

Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship.

Effect of the static magnetic field of the MR-scanner on ERPs: Evaluation of visual, cognitive and motor potentials

OBJECTIVE This work investigates the influence of the static magnetic field of the MR-scanner on ERPs extracted from simultaneous EEG-fMRI recordings. The quality of the ERPs after BallistoCardioGraphic (BCG) artifact removal, as well as the reproducibility of the waveforms in different environments is investigated. METHODS We consider a Detection, a Go-Nogo and a Motor task, eliciting peaks that differ in amplitude, latency and scalp topography, repeated in two situations: outside the scanner room (0T) and inside the MR-scanner but without gradients (3T). The BCG artifact is removed by means of three techniques: the Average Artifact Subtraction (AAS) method, the Optimal Basis Set (OBS) method and the Canonical Correlation Analysis (CCA) approach. RESULTS The performance of the three methods depends on the amount of averaged trials. Moreover, differences are found on both amplitude and latency of ERP components recorded in two environments (0T vs 3T). CONCLUSIONS We showed that, while ERPs can be extracted from simultaneous EEG-fMRI data at 3T, the static magnetic field might affect the physiological processes under investigation. SIGNIFICANCE The reproducibility of the ERPs in different recording environments (0T vs 3T) is a relevant issue that deserves further investigation to clarify the equivalence of cognitive processes in both behavioral and imaging studies.

Convergence and translation: attitudes to inter-professional learning and teaching of creative problem-solving among medical and engineering students and staff.

BackgroundHealthcare worldwide needs translation of basic ideas from engineering into the clinic. Consequently, there is increasing demand for graduates equipped with the knowledge and skills to apply interdisciplinary medicine/engineering approaches to the development of novel solutions for healthcare. The literature provides little guidance regarding barriers to, and facilitators of, effective interdisciplinary learning for engineering and medical students in a team-based project context.MethodsA quantitative survey was distributed to engineering and medical students and staff in two universities, one in Ireland and one in Belgium, to chart knowledge and practice in interdisciplinary learning and teaching, and of the teaching of innovation.ResultsWe report important differences for staff and students between the disciplines regarding attitudes towards, and perceptions of, the relevance of interdisciplinary learning opportunities, and the role of creativity and innovation. There was agreement across groups concerning preferred learning, instructional styles, and module content. Medical students showed greater resistance to the use of structured creativity tools and interdisciplinary teams.ConclusionsThe results of this international survey will help to define the optimal learning conditions under which undergraduate engineering and medicine students can learn to consider the diverse factors which determine the success or failure of a healthcare engineering solution.

Steady State Visual Evoked Potential (SSVEP) - Based Brain Spelling System with Synchronous and Asynchronous Typing Modes

The paper presents an EEG-based wireless brain-computer interface (BCI) with which subjects can mind-spell text on a computer screen. The application is based on the detection of steady-state visual evoked potentials (SSVEP) in EEG signals recorded on the scalp of the subject. The performance of the BCI is compared for two different classification paradigms, called synchronous and asynchronous modes.

BOLD correlates of Alpha and Beta EEG-rhythm during a motor task

In this study, simultaneously acquired EEG and fMRI data from a motor experiment are analyzed. The motor task consists in moving the right hand and is performed by a group of healthy volunteers. The objective is to find the most adequate way to model the movement-related blood oxygen level-dependent (BOLD) response present in the fMRI data. The analysis of the fMRI data is performed using Statistical Parametric Mapping (SPM) and estimating two different models. In the first one (motor event model), the BOLD response is modeled following the time instants of the motor events. The second one (brain wave model) incorporates the dynamics of the 5 canonical EEG rhythms (a, p, y, 8, 6) to describe the BOLD response. From the results, it can be concluded that the motor event model better describes the BOLD response related to the movement itself, but that the brain wave model is better suited to characterize the BOLD response of complementary brain processes.