Search Results for author:
Found 64 papers, 19 papers with code
Automated generation of epilepsy surgery resection masks; The RAMPS pipeline
We propose RAMPS (Resections And Masks in Preoperative Space), an automated pipeline to generate a 3D resection mask of pre-operative tissue.
From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping
We envisage that this dataflow guide will not only increase understanding and application of normative mapping methods, but will also improve the consistency and quality of studies in the field.
Efficient Task Grouping Through Samplewise Optimisation Landscape Analysis
Shared training approaches, such as multi-task learning (MTL) and gradient-based meta-learning, are widely used in various machine learning applications, but they often suffer from negative transfer, leading to performance degradation in specific tasks.
More variable circadian rhythms in epilepsy: a retrospective cross-sectional study using long-term heart rate recordings from wearable sensors
Interpretation: This finding indicates that the circadian rhythm of heart rate is more variable for people with epilepsy and that this can be detected using a wearable device.
Comparing Methodological Variations in Seizure Onset Localisation Algorithms using intracranial EEG
In this work we investigate how methodological differences in such algorithms can result in different onset locations being identified.
Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities
Current clinical approaches to identify structural abnormalities for surgical targeting in epilepsy do not use diffusion-weighted MRI (dMRI), despite evidence that dMRI abnormalities are present in epilepsy and may relate to the epileptogenic zone.
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation
Our key discovery is that coarse-grained noises in earlier denoising steps have demonstrated high motion consistency across consecutive video frames.
Anti-seizure medication load is not correlated with early termination of seizure spread
Our objective was to test if this effect was due to limiting seizure spread through early termination of otherwise unchanged seizures.
Status epilepticus and thinning of the entorhinal cortex
Individuals with TLE and SE had reduced entorhinal thickness compared to those with TLE and no history of SE.
The Imaging Database for Epilepsy And Surgery (IDEAS)
To demonstrate the veracity of our data, we successfully replicated previous studies showing long-term outcomes of seizure freedom in the range of around 50%.
Vagus nerve stimulation: Laying the groundwork for predictive network-based computer models
Vagus Nerve Stimulation (VNS) is an established palliative treatment for drug resistant epilepsy.
Brain Morphology Normative modelling platform for abnormality and Centile estimation: Brain MoNoCle
These models can then be applied to e. g. smaller clinical cohorts to distinguish disease effects from other covariates.
Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
To describe such modular-level learning capabilities, we introduce a novel concept dubbed modular neural tangent kernel (mNTK), and we demonstrate that the quality of a module's learning is tightly associated with its mNTK's principal eigenvalue $\lambda_{\max}$.
Unsupervised Machine Learning Identifies Latent Ultradian States in Multi-Modal Wearable Sensor Signals
Wearable sensors such as smartwatches have become ubiquitous in recent years, allowing the easy and continual measurement of physiological parameters such as heart rate, physical activity, body temperature, and blood glucose in an every-day setting.
Anti-seizure medication tapering correlates with daytime delta band power reduction in the cortex
This dose-dependent effect was evident across all recorded cortical regions during daytime; but not in sub-cortical regions, or during night time.
Multi-centre normative brain mapping of intracranial EEG lifespan patterns in the human brain
Results: Recording hospital significantly impacted normative icEEG maps in all frequency bands, and age was a more influential predictor of band power than sex.
Alpha rhythm slowing in temporal epilepsy across Scalp EEG and MEG
Alpha slowing in TLE was found in both EEG and MEG recordings.
Incomplete resection of the icEEG seizure onset zone is not associated with post-surgical outcomes
Retrospective analysis of icEEG recordings from 63 subjects (735 seizures) identified seizure onset regions through visual inspection and algorithmic delineation.
Multiscale cortical morphometry reveals pronounced regional and scale-dependent variations across the lifespan
Most studies to date have found a monotonic decrease in commonly used morphometrics, such as cortical thickness and volume, across the entire brain with increasing age.
Multivariate brain-cognition associations in euthymic bipolar disorder
Results: BD showed impairments on tests of PS, attention, and EF, and abnormal cortical thickness in several brain regions compared to healthy controls.
Aperiodic MEG abnormality in patients with focal to bilateral tonic-clonic seizures
Here, we hypothesised greater aperiodic abnormality in the epileptogenic zone, in patients with focal to bilateral tonic clonic (FBTC) seizures, and earlier age of seizure onset.
Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra
Combining periodic and aperiodic abnormalities improved performance, similar to the complete band power approach.
Diminished circadian and ultradian rhythms of human brain activity in pathological tissue in vivo
Chronobiological rhythms, such as the circadian rhythm, have long been linked to neurological disorders, but it is currently unknown how pathological processes affect the expression of biological rhythms in the brain.
Medical records condensation: a roadmap towards healthcare data democratisation
The prevalence of artificial intelligence (AI) has envisioned an era of healthcare democratisation that promises every stakeholder a new and better way of life.
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.
Interictal MEG abnormalities to guide intracranial electrode implantation and predict surgical outcome
Finally, we assessed if the implantation of electrodes in abnormal tissue, and resection of the strongest abnormalities determined by MEG and iEEG explained surgical outcome.
Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power
We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesising a greater overlap in seizure-free patients.
Normative brain mapping using scalp EEG and potential clinical application
Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results.
Complementary structural and functional abnormalities to localise epileptogenic tissue
Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation and resection of the epileptogenic zone (EZ), and improve surgical outcomes in epilepsy.
Temporal stability of intracranial EEG abnormality maps for localising epileptogenic tissue
To assess the localising value of band power abnormality, we computed DRS - a measure of how different the surgically resected and spared tissue were in terms of band power abnormalities - over time.
Effects of anterior temporal lobe resection on cortical morphology
Here, we examine the effects of this surgery on cortical morphology measured in independent variables both near the resection and remotely.
Neuro-evolutionary evidence for a universal fractal primate brain shape
To demonstrate the importance of this new understanding, we show a scale-dependent effect of ageing on brain morphology.
Training Strategies for Improved Lip-reading
In this paper, we systematically investigate the performance of state-of-the-art data augmentation approaches, temporal models and other training strategies, like self-distillation and using word boundary indicators.
Ranked #2 on
Lipreading
on Lip Reading in the Wild
(using extra training data)
MEG abnormalities and mechanisms of surgical failure in neocortical epilepsy
Resting-state MEG data were acquired for 70 healthy controls and 32 patients with refractory neocortical epilepsy.
A library of quantitative markers of seizure severity
In individual patients, 71% had a moderate to large difference (ranksum r > 0. 3) between focal and subclinical seizures in three or more markers.
Intracranial EEG structure-function coupling predicts surgical outcomes in focal epilepsy
We investigated pre-operative structure-function coupling at two spatial scales a) at the global iEEG network level and b) at the resolution of individual iEEG electrode contacts using virtual surgeries.
Self-supervised Video-centralised Transformer for Video Face Clustering
We also investigate face clustering in egocentric videos, a fast-emerging field that has not been studied yet in works related to face clustering.
Ranked #1 on
Face Clustering
on EasyCom
Volumetric and structural connectivity abnormalities co-localise in TLE
We computed grey matter volumetric changes and white matter structural connectivity abnormalities in 144 patients with unilateral TLE and 96 healthy controls.
Chronic iEEG recordings and interictal spike rate reveal multiscale temporal modulations in seizure states
We then compared SNS occurrence and duration to (1) time since implantation and (2) patient-specific circadian and multidien cycles in interictal spike rate.
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices
Experimental studies with 20 participants demonstrate that, thanks to the emotionship awareness, EMOShip not only achieves superior emotion recognition accuracy over existing methods (80. 2% vs. 69. 4%), but also provides a valuable understanding of the cause of emotions.
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups
We then study the effect of variety and number of age-groups used during training on generalisation to unseen age-groups and observe that an increase in the number of training age-groups tends to increase the apparent emotional facial expression recognition performance on unseen age-groups.
Facial Expression Recognition
Facial Expression Recognition (FER)
Seizure pathways and seizure durations can vary independently within individual patients with focal epilepsy
However, it is unclear whether seizures following the same pathway will have the same duration or if these features can vary independently.
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
To evaluate our method on in-the-wild data, we also introduce a new challenging large-scale benchmark called IMDB-Clean.
Ranked #1 on
Age Estimation
on KANFace
Normative brain mapping of interictal intracranial EEG to localise epileptogenic tissue
We then compared interictal recordings from 62 patients with epilepsy to the normative map to identify abnormal regions.
Long-term changes in functional connectivity predict responses to intracranial stimulation of the human brain
Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions.
MemX: An Attention-Aware Smart Eyewear System for Personalized Moment Auto-capture
MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets.
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline
By monitoring the impact of varying resolution on the quality of high-dimensional video analytics features, hence the accuracy of video analytics results, the proposed end-to-end optimization framework learns the best non-myopic policy for dynamically controlling the resolution of input video streams to globally optimize energy efficiency.
Local Morphological Measures Confirm that Folding within Small Partitions of the Human Cortex Follows Universal Scaling Law
The universal scaling law of cortical morphology describes cortical folding as the covariance of average grey matter thickness, pial surface area, and exposed surface area.
RoI Tanh-polar Transformer Network for Face Parsing in the Wild
Face parsing aims to predict pixel-wise labels for facial components of a target face in an image.
Ranked #1 on
Face Parsing
on iBugMask
Fluctuations in EEG band power at subject-specific timescales over minutes to days explain changes in seizure evolutions
These results suggest that subject-specific fluctuations in iEEG band power over timescales of minutes to days may serve as markers of seizure modulating processes.
Generalization Bound of Gradient Descent for Non-Convex Metric Learning
Metric learning aims to learn a distance measure that can benefit distance-based methods such as the nearest neighbour (NN) classifier.
Lip-reading with Densely Connected Temporal Convolutional Networks
In this work, we present the Densely Connected Temporal Convolutional Network (DC-TCN) for lip-reading of isolated words.
Dilated Convolutions with Lateral Inhibitions for Semantic Image Segmentation
Introducing LI mechanisms improves the convolutional filter's sensitivity to semantic object boundaries.
Reliability and comparability of human brain structural covariance networks
In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults.
Neurons and Cognition Quantitative Methods
Shape Constrained Network for Eye Segmentation in the Wild
Specifically, we learn the shape prior from our dataset using VAE-GAN, and leverage the pre-trained encoder and discriminator to regularise the training of SegNet.
Dynamic Face Video Segmentation via Reinforcement Learning
To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.
End-to-End Visual Speech Recognition for Small-Scale Datasets
In this work, we present an end-to-end visual speech recognition system based on fully-connected layers and Long-Short Memory (LSTM) networks which is suitable for small-scale datasets.
Face Mask Extraction in Video Sequence
Inspired by the recent development of deep network-based methods in semantic image segmentation, we introduce an end-to-end trainable model for face mask extraction in video sequence.
Fractal and multifractal properties of electrographic recordings of human brain activity
Using intracranially recorded EEG and simulated data, we compared monofractal and multifractal methods with regards to their sensitivity to signal variance.
Neurons and Cognition Statistical Mechanics Information Theory Information Theory Adaptation and Self-Organizing Systems Chaotic Dynamics
A real-time and unsupervised face Re-Identification system for Human-Robot Interaction
In this paper, we present an effective and unsupervised face Re-ID system which simultaneously re-identifies multiple faces for HRI.
Learning Local Metrics and Influential Regions for Classification
The performance of distance-based classifiers heavily depends on the underlying distance metric, so it is valuable to learn a suitable metric from the data.
End-to-End Audiovisual Fusion with LSTMs
To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the pixels and spectrograms and perform classification of speech and nonlinguistic vocalisations.
End-to-End Multi-View Lipreading
To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and performs visual speech classification from multiple views and also achieves state-of-the-art performance.
