EEG study investigating how children maintain and implement task goals. Revealed neural markers of cognitive control development and individual differences in executive function.
- 64-channel EEG
- Event-Related Potentials
- Developmental Psychology
Cognitive Neuroscience
Understanding cognitive development through EEG and behavioral research.
My neuroscience research focuses on working memory, attention, and cognitive control in children. Using EEG and behavioral assessments, I investigate how these systems develop and relate to conditions like ADHD.
Neural Signal Processing
EEG Research in Cognitive Development
My research uses electroencephalography to understand how children's brains develop cognitive control, working memory, and attention. This work has implications for understanding ADHD and other developmental conditions.
Research examining the relationship between working memory capacity and ADHD symptoms. Used both neurological and behavioural assessments to identify cognitive markers.
- Cognitive Assessment
- ADHD Research
- Clinical Psychology
Investigated neurological and cognitive effects of dexamphetamine treatment in ADHD. Examined changes in attention, working memory, and neural activity patterns.
- Pharmacological Study
- EEG Analysis
- Cognitive Testing
Brain-Computer Interfaces
Thought as Interface
Creating direct neural pathways between minds and machines. These systems translate intention into action, opening new channels for communication and control.
Motor Imagery Control
Developed a non-invasive BCI system that enables quadriplegic patients to control robotic arms through imagined movements. The system learns each user's unique neural patterns through adaptive calibration.
Real-time signal processing achieves sub-100ms latency from thought to action, while machine learning continuously refines the mapping between neural patterns and intended movements.
BCI
Motor Cortex
Rehabilitation
Cognitive Load Monitor
Built wearable EEG systems that track mental workload in real-time, adjusting task complexity to maintain optimal cognitive engagement. Used in educational settings to personalise learning pace.
The system combines frontal theta, parietal alpha, and gamma synchrony to create a multidimensional model of attention, fatigue, and comprehension.
Wearable EEG
Adaptive Learning
Attention
Brain Mapping
The Connectome Project
Visualising the intricate wiring of the human brain through diffusion tensor imaging and graph theory. Each connection tells a story about information flow and functional organisation.
- Nodes Mapped
- 86B
- Connections Traced
- 100T
- Resolution
- 1.25mm³
This living atlas reveals how neural highways support everything from perception to memory, showing how damage to specific pathways manifests as cognitive changes.
Explore Research →Computational Neuroscience
Modeling Mind
Building mathematical models that capture the dynamics of neural computation, from single neurons to whole-brain networks.
Hierarchical prediction error in visual cortex
Developed computational models showing how the brain minimizes prediction error across cortical hierarchies. The framework explains visual illusions, attention, and perceptual learning through a unified principle.
Cross-frequency coupling in memory formation
Discovered how theta-gamma coupling in the hippocampus coordinates memory encoding. Built biophysical models showing how this rhythm emerges from inhibitory interneuron networks.
Neural avalanches and optimal information processing
Demonstrated that healthy brains operate near critical phase transitions, maximising information transmission and dynamic range. Developed tools to measure criticality from MEG recordings.
Neuroimaging Innovation
Seeing Thought in Motion
Advancing imaging techniques to capture neural dynamics with unprecedented spatial and temporal resolution.
Integrated magnetoencephalography with functional MRI to achieve millisecond temporal resolution with millimeter spatial precision, revealing the spatiotemporal cascade of sensory processing.
- MEG
- fMRI
- Source Localization
Developed lightweight, wireless EEG systems for naturalistic neuroscience. Subjects can move freely while we record brain activity during real-world tasks and social interactions.
- Mobile EEG
- Wireless
- Ecological Validity
Pioneered optical techniques for recording from thousands of neurons simultaneously in behaving animals, capturing the full dynamics of local circuits during decision-making.
- Two-Photon
- Calcium Imaging
- Holographic
Clinical Translation
From Lab to Life
Translating neuroscience discoveries into clinical tools that improve diagnosis and treatment of neurological conditions.
Epilepsy Prediction System
Machine learning models that predict seizures hours in advance by detecting subtle changes in EEG dynamics. The system alerts patients via smartphone, allowing them to take preventive measures.
Clinical trials showed 85% sensitivity with less than one false alarm per day, dramatically improving quality of life for patients with refractory epilepsy.
Depression Biomarkers
Identified neural signatures of treatment-resistant depression using resting-state connectivity analysis. These biomarkers predict response to different interventions, enabling personalized treatment selection.
The approach combines graph theory metrics with machine learning to stratify patients into neurobiologically distinct subtypes, each responding to different therapeutic approaches.
Open Science Initiative
Democratizing Neuroscience
Making neuroscience tools and data accessible to researchers worldwide through open-source software and public datasets.
"The open-source EEG analysis toolkit has transformed how our lab processes data. What used to take weeks now takes hours, and the visualizations help us see patterns we'd missed before."
Neuroscience PhD · Stanford"Their public dataset of simultaneous EEG-fMRI recordings has become the gold standard for methods development. It's enabled entirely new research directions in our field."
Professor · MIT Brain Lab"The educational materials make complex neuroscience accessible without dumbing it down. My students finally understand signal processing through the interactive tutorials."
Lecturer · Oxford Neuroscience