Topic: electroencephalography

Fast periodic visual stimulation (FPVS) has emerged as a promising tool for assessing cognitive function in individuals with dementia. This technique leverages electroencephalography (EEG) to measure brain responses to rapidly presented visual stimuli, offering a non-invasive and objective method for evaluating a range of cognitive functions. Unlike traditional cognitive assessments, FPVS does not rely on behavioural responses, making it particularly suitable for individuals with cognitive impairment. In this talk I will highlight a series of studies that have demonstrated its ability to detec

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

Advanced noninvasive neuroimaging methods provide valuable information on the brain function, but they have obvious pros and cons in terms of temporal and spatial resolution. Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) effect provides good spatial resolution in the order of millimeters, but has a poor temporal resolution in the order of seconds due to slow hemodynamic responses to neuronal activation, providing indirect information on neuronal activity. In contrast, electroencephalography (EEG) and magnetoencephalography (MEG) provide excellent

Repetitive spatiotemporal patterns in resting-state brain activities have been widely observed in various species and regions, such as rat and cat visual cortices. Since they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. Moreover, spatiotemporal patterns involving whole-brain activities may also reflect a process that integrates information distributed over the entire brain, such as motor and visual information. Therefore, revealing such patterns may elucidate how the information is integrated to generate consc

Fastball is a novel, fast, passive biomarker of cognitive function, that uses cheap, scalable electroencephalography (EEG) technology. It is sensitive to early dementia; language, education, effort and anxiety independent and can be used in any setting including patients’ homes. It can capture a range of cognitive functions including semantic memory, recognition memory, attention and visual function. We have shown that Fastball is sensitive to cognitive dysfunction in Alzheimer’s disease and Mild Cognitive Impairment, with data collected in patients’ homes using low-cost portable EEG. We are n

In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby optimizing behavior. One of the fundamental questions in neuroscience concerns the neural computations that underlie these probabilistic sensorimotor processing. Through a recurrent neural network (RNN) model and human psychophysics and electroencephalography (EEG), the present study investigates circuit mechanisms for processing probabilistic structures of sensory signals to guide behavior. We first constructe

The human brain is a heavily interconnected structure giving rise to complex functions. While brain functionality is mostly revealed during wakefulness, the sleeping brain might offer another view into physiological and pathological brain connectivity. Furthermore, there is a large body of evidence supporting that sleep mediates plastic changes in brain connectivity. Although brain plasticity depends on environmental input which is provided in the waking state, disconnection during sleep might be necessary for integrating new into existing information and at the same time restoring brain effic

Although electroencephalography (EEG) has been used in clinical and research studies for almost a century, recent technological advances have made the equipment and processing tools more accessible outside laboratory settings. These low-cost alternatives can achieve satisfactory results in experiments such as detecting event-related potentials and classifying cognitive states. In our research, we use low-cost single-channel EEG to classify brain activity during the presentation of images of opposite emotional valence from the OASIS database. Emotional classification has already been achieved u

Dr Jennifer Lawlor Title: Tracking changes in complex auditory scenes along the cortical pathway Complex acoustic environments, such as a busy street, are characterised by their everchanging dynamics. Despite their complexity, listeners can readily tease apart relevant changes from irrelevant variations. This requires continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is

Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on

Robotic-assisted rehabilitation therapies play a crucial role in improving motor recovery by providing precise, repeatable, and intensive therapeutic interventions that traditional methods often lack [1,2,3]. Nonetheless, their adaptability is restricted due to a lack of comprehensive data on patients' biophysical signals and behavior [4,5,6]. Developing intelligent control mechanisms for rehabilitation devices is an essential step to enable personalized and responsive training for patients. This research project introduces the development of an innovative multimodal decoder for hybrid Brain-

Sustained attention is the focusing attention process on the selected stimulus for a long time, which can be measured with sustained attention to response task (SART). Our research group aimed to investigate the age-related changes in inhibitory functions with electroencephalography (EEG) in theta oscillations. The EEG of 19 healthy individuals (young, 18-25 years old, M = 21.71, SD = 2.06; elderly 55-70 years old, M = 62.43 , SD = 6.33) was recorded at the F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, Oz, and O2 locations during the auditory SART. The maximum power of the target stimuli was calcula

Working memory is inherently limited in capacity and duration, which may hamper learning, including in online learning environments. Because working memory capacity varies among individuals, cognitive load in online learning may not have the same impact for neurotypical and neurodivergent students. This study investigated the relationship between neurodivergent traits and cognitive load during online learning. Seventy participants, including 35 neurotypical students and 35 neurodivergent students with a diagnosis of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD