Katrien Vanderperren

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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(More)
Simultaneous EEG-fMRI measurements can combine the high spatial resolution of fMRI with the high temporal resolution of EEG. Therefore, we applied this approach to the study of peripheral vision. More specifically, we presented visual field quadrant fragments of checkerboards and a full central checkerboard in a simple detection task. A technique called(More)
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(More)
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(More)
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(More)
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(More)
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(More)
— 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 detecting 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:(More)
To fully understand the cognitive processes occurring in the human brain, high resolution in both spatial and temporal information is needed. Most neuroimaging approaches, however, only possess high accuracy in one of these two domains. Therefore, the multimodal analysis of brain activity is becoming more and more popular among the research community. One(More)
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(More)