EEG

…and wearable technologies, including EEG, intracortical electrophysiology, EMG, MEG and ECG, now yield large and diverse biosignal datasets collected across real-world contexts, but these timeseries are noisy, heterogeneous and recorded under variable conditions across subjects, devices and environments. The workshop brings together neuroscientists, biomedical engineers, wearable-technology researchers and ma

Investigate how sleep shapes human perception and cognition using MRI and EEG. The appointment includes development of methods for three-tesla and seven-tesla MRI and multimodal studies of the sleeping brain.

Traditionally, touch is associated with exteroception and is rarely considered a relevant sensory cue for controlling movements in space, unlike vision. We developed a technique to isolate and measure tactile involvement in controlling sliding finger movements over a surface. Young adults traced a 2D shape with their index finger under direct or mirror-reversed visual feedback to create a conflict between visual and somatosensory inputs. In this context, increased reliance on somatosensory input compromises movement accuracy. Based on the hypothesis that tactile cues contribute to guiding hand

…Visual Stimulation (FPVS) with EEG among 16 students and 12 police officers with normal face processing abilities. Participants' neural responses to a 1-Hz tagged oddball identity embedded within a 6-Hz image stream revealed implicit recognition with high-quality mugshots but not CCTV-like images, suggesting optimal resolution requirements. Our findings extend previous research by demonstrating that even unfamiliar identities can elicit robust neural recognition signatures through brief, repeated passive

…a shared task measured with EEG, MEG and fMRI, evidence about location, duration and inter-area communication, and the practical difficulties of interpreting mixed results across competing theories.

The brain exhibits rich rhythmic activity generated by synchronous neural firing. These neural rhythms, which possess a variety of spatiotemporal patterns, are linked to cognition and vigilance. Neural rhythms are a defining feature of arousal states: in humans, alpha (8-12Hz) is a key marker of quiet wakefulness, and is associated with attentional processes, while delta (1-4Hz) is the most prominent rhythm during non-REM sleep, and is tied to memory consolidation and brain waste clearance. Prior work sought to localize the oscillatory generators of specific rhythms, but it is not clear what b

Lapses in attention are ubiquitous and, unfortunately, the cause of many tragic accidents. One potential solution may be to develop assistance systems which can use objective, physiological signals to monitor attention levels and predict a lapse in attention before it occurs. As it stands, it is unclear which physiological signals are the most reliable markers of inattention, and even less is known about how reliably they will work in a more naturalistic setting. My project aims to address these questions across two experiments: a lab-based experiment and a more ‘real-world’ experiment. In thi

Electroencephalography (EEG) has been thoroughly studied for decades in psychiatry research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patient's heterogeneity, overlooked in psychiatric EEG research relying on a case-control approach. We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity- across a cohort of 1674 patients with attention-deficit/hyperacti

Advancing therapies for neurological disorders relies on developing efficient, personalized whole-brain models that can capture mechanistic insights and pathological features. Current challenges in creating such computational models arise from managing a considerable number of adjustable parameters. The conventional manual exploration of this parameter space is a serious limitation for personalizing models to individual subject data [1]. This work aims to address this by approaching the problem as an optimization challenge in the context of developing brain models capable of characterizing in

Long-range temporal dependencies (LRTD) are an important signature of computation in the brain. They were shown to be decreased by Alzheimer’s and increased by epilepsy. An established way of capturing the LRTDs is to use Hurst exponents, for which estimation various methodologies were proposed in different fields of study. In neuroscience, LRTDs have been measured in broad-band electro- and magnetoencephalography (E/MEG) [2] (Fig. 1h), amplitude envelopes of oscillations [1] (Fig. 1g), and functional Magnetic Resonance Imaging (fMRI) [3] (Fig. 1f) using several existing methods. Theoretical w

…as spikes, local field potentials and EEG.

Frontal alpha asymmetry in Major Depressive Disorder has been widely examined in EEG research. Recently, the EEG has also been suggested as a potential diagnostic biomarker for Seasonal Affective Disorder (SAD), a recurrent Major Depressive Disorder with a seasonal pattern. However, thus far no studies have been conducted in the dark Icelandic winter to examine whether frontal alpha asymmetry can be detected also among individuals with elevated risk for seasonality. EEG data was recorded in 122 participants (34 men) during rest (3 minutes) in spring, summer, fall and winter in Iceland. At each

…deep stimulation electrodes and with EEG electrodes placed in the motor areas, in order to provide simultaneous recording from both the subthalamic nucleus (STN) and the cortex. Then, we used a dynamical mea

…to fit neurophysiological signals like EEG and MEG, in particular by optimizing interactions between brain regions, following previous work on the dynamic causal model (DCM) . We compare several modern Bayesian inference schemes for model inversion on simulated data . We benchmark the identifiability of local parameters governing the interplay between several neuronal populations in each region, as well as global parameters t

…in dynamic environments through EEG and pupillometry. We had 26 young adults perform an auditory motion discrimination task, indicating the final directions of auditory motion sequences with ran

…(exoskeleton robot, Kinarm Lab) and EEG of 60 participants were recorded to investigate direction-specific adaptation du

Cognitive-motor (C-M) interference is a phenomenon during which demands from concurrent cognitive and motor tasks create competition for available neural resources. Research introduces this competition using dual-task designs to compare performance on single-task situations to dual-tasks, which require both motor and cognitive engagement. However, while some individuals experience C-M interference, others have a more integrative effect, presenting as better adaptation to multi-modal demands. Little is known about how this C-M relationship develops, nor what factors play a role in the integrati

…previously used to identify an EEG signature of motor-independent evidence accumulation (O'Connell, Dockree, & Kelly 2012), in an fMRI study. It involved a mental counting task assessing vi

Neural population signals recorded with magneto- (MEG) or electroencephalography (EEG) contain information about viewed natural images and their categories. However, it remains unclear which specific visual features contribute to this information. To address this, we applied spectral manipulations and different texturization algorithms to natural images of various categories and recorded MEG in human subjects presented with these stimuli. The employed stimulus space allowed us to attribute components of visual information to specific low-, mid- and high-level visual features. Furthermore, this

…belief updating, we conducted a human EEG experiment (N = 29) investigating auditory spatial localization in noisy volatile environments and analyzed the ass

Acquiring a new skill, such as playing the piano, initially demands significant cognitive control. As one becomes proficient, reliance on cognitive control reduces, enabling skill automation. In adults, this transition is behaviourally evident through improved response times and accuracy, and neurally, by reduced frontal theta power across learning. However, our understanding of this transition throughout developmental stages is limited, especially concerning children and adolescents' ability to disengage cognitive control for skill automation. To address this gap, we measured neural activity

Decision models involving protracted temporal integration of sensory information are known to provide excellent fits to decision behaviour. However, despite the computational advantage in principle, several studies have suggested a limited role for extended integration in practice, variously finding that it is subjected to low bounds, applied over a short timeframe, or plausibly replaced by non-integration strategies such as extrema detection. Here, we probed the role of extended integration in a noiseless but difficult contrast comparison task with response choices made after viewing a long 1

…identified post-feedback signals from EEG and pupillometry that captured differential a

…be captured by electroencephalography (EEG) or magnetoencephalography (MEG), termed speech-FFR. While EEG measurements have found a subcortically evoked early neural activity to this speech-FFR, MEG studies have recently emphasized involvement of cortical sources as well. Here, we focused on disentangling the sources of the neural response at the fundamental frequency of speech using EEG-based source localization. We theref