4 citations · 10 across the 21 of their papers we have counts for
21 papers
Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning
Stella Ho, Joel Villalobos, Joseph West +7
ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity. This study examines the feasibility of vis…
Adaptive Segmentation of EEG for Machine Learning Applications
Johnson Zhou, Joseph West, Krista A. Ehinger +3
Objective. Electroencephalography (EEG) data is derived by sampling continuous neurological time series signals. In order to prepare EEG signals for machine learning, the signal mu…
Minimally Invasive Brain Computer Interfaces: Evaluating the Impact of Tissue Layers on Signal Quality of Sub-Scalp EEG
Timothy B Mahoney, JingYang Liu, Huakun Xin +2
Individuals with severe physical disabilities often experience diminished quality of life stemming from limited ability to engage with their surroundings. Brain-Computer Interface…
Sub-Scalp EEG for Sensorimotor Brain-Computer Interface
Timothy B Mahoney, David B Grayden, Sam E John
Objective: To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating it…
Decoding Saccadic Eye Movements from Brain Signals Using an Endovascular Neural Interface
Suleman Rasheed, James Bennett, Peter E. Yoo +2
An Oculomotor Brain-Computer Interface (BCI) records neural activity from regions of the brain involved in planning eye movements and translates this activity into control commands…
Sub-Scalp Brain-Computer Interface Device Design and Fabrication
Timothy B. Mahoney, David B. Grayden, Sam E. John
Current brain-computer interfaces (BCI) face limitations in signal acquisition. While sub-scalp EEG offers a potential solution, existing devices prioritize chronic seizure monitor…
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation
John S. Russo, Thomas A. Shiels, Chin-Hsuan Sophie Lin +2
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This…
Towards Developing Brain-Computer Interfaces for People with Multiple Sclerosis
John S. Russo, Tim Mahoney, Kirill Kokorin +4
Multiple Sclerosis (MS) is a severely disabling condition that leads to various neurological symptoms. A Brain-Computer Interface (BCI) may substitute some lost function; however,…
Epileptic seizure forecasting with long short-term memory (LSTM) neural networks
Daniel E. Payne, Jordan D. Chambers, Anthony Burkitt +4
Objective: Forecasting epileptic seizures can reduce uncertainty for patients and allow preventative actions. While many models can predict the occurrence of seizures from features…
Path Signatures for Seizure Forecasting
Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +4
Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epi…
Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface
Timothy B. Mahoney, Po-Chen Liu, David B Grayden +1
Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method th…
Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT
Parvin Zarei Eskikand, David B Grayden, Tatiana Kameneva +2
Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by wh…
Autoregressive models for biomedical signal processing
Jonas F. Haderlein, Andre D. H. Peterson, Anthony N. Burkitt +2
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is,…
On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case
Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +3
The empirical success of machine learning models with many more parameters than measurements has generated an interest in the theory of overparameterisation, i.e., underdetermined…
Brain Model State Space Reconstruction Using an LSTM Neural Network
Yueyang Liu, Artemio Soto-Breceda, Yun Zhao +5
Objective Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to EEG. However, this approach lacks a reliab…
On the benefit of overparameterization in state reconstruction
Jonas F. Haderlein, Iven M. Y. Mareels, Andre Peterson +3
The identification of states and parameters from noisy measurements of a dynamical system is of great practical significance and has received a lot of attention. Classically, this…
Frequency Superposition -- A Multi-Frequency Stimulation Method in SSVEP-based BCIs
Jing Mu, David B. Grayden, Ying Tan +1
The steady-state visual evoked potential (SSVEP) is one of the most widely used modalities in brain-computer interfaces (BCIs) due to its many advantages. However, the existence of…
Multi-Frequency Canonical Correlation Analysis (MFCCA): A Generalised Decoding Algorithm for Multi-Frequency SSVEP
Jing Mu, Ying Tan, David B. Grayden +1
Stimulation methods that utilise more than one stimulation frequency have been developed for steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) with the…
Impact of axonal delay on structure development in a multi-layered network
Catherine E Davey, David B Grayden, Anthony N Burkitt
The mechanisms underlying how activity in the visual pathway may give rise through neural plasticity to many of the features observed experimentally in the early stages of visual p…
Emergence of radial orientation selectivity: Effect of cell density changes and eccentricity in a layered network
Catherine E. Davey, David B. Grayden, Anthony N. Burkitt
Previous work by Linsker revealed how simple cells can emerge in the absence of structured environmental input, via a self-organisation learning process. He empirically showed the…
A homotopic mapping between current-based and conductance-based synapses in a mesoscopic neural model of epilepsy
Andre D. H. Peterson, Hamish Meffin, Mark J. Cook +3
Changes in brain states, as found in many neurological diseases such as epilepsy, are often described as bifurcations in mesoscopic neural models. Nearly all of these models rely o…