Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing

MP Hosseini, TX Tran, D Pompili, K Elisevich… - Artificial Intelligence in …, 2020 - Elsevier
Background and objective Multimodal data analysis and large-scale computational
capability is entering medicine in an accelerative fashion and has begun to influence …

Fast imaging for mapping dynamic networks

P LeVan, B Akin, J Hennig - NeuroImage, 2018 - Elsevier
The development of highly accelerated fMRI acquisition techniques has led to novel
possibilities to monitor cerebral activity non-invasively and with unprecedented temporal …

The psychophysiology of action: A multidisciplinary endeavor for integrating action and cognition

S Hoffmann, U Borges, L Bröker, S Laborde… - Frontiers in …, 2018 - frontiersin.org
There is a vast amount of literature concerning the integration of action and cognition.
Although this broad research area is of great interest for many disciplines like sports …

LLM-Enhanced Multi-Teacher Knowledge Distillation for Modality-Incomplete Emotion Recognition in Daily Healthcare

Y Zhang, H Liu, Y Xiao, M Amoon… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
The critical importance of monitoring and recognizing human emotional states in healthcare
has led to a surge in proposals for EEG-based multimodal emotion recognition in recent …

A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data

M Tajmirriahi, H Rabbani - Journal of Medical Signals & Sensors, 2024 - journals.lww.com
Unexpected seizures significantly decrease the quality of life in epileptic patients. Seizure
attacks are caused by hyperexcitability and anatomical lesions of special regions of the …

A new generation of brain-computer interfaces driven by discovery of latent EEG-fMRI linkages using tensor decomposition

G Deshpande, D Rangaprakash, L Oeding… - Frontiers in …, 2017 - frontiersin.org
A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by
decoding brain activity. Electroencephalography (EEG) has been extensively used for …

Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

NCD Truong, X Wang, H Wanniarachchi, Y Lang… - Scientific Reports, 2022 - nature.com
Decision-making is one of the most critical activities of human beings. To better understand
the underlying neurocognitive mechanism while making decisions under an economic …

The effects of a virtual reality rehabilitation task on elderly subjects: An experimental study using multimodal data

J Qu, L Cui, W Guo, X Ren, L Bu - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Ageing populations are becoming a global issue. Against this background, the assessment
and treatment of geriatric conditions have become increasingly important. This study draws …

[HTML][HTML] Scalp surface Laplacian potential monitoring system based on novel hydrogel active tri-polar concentric ring electrodes

H Hua, B Feng, Z Yuan, Q Xiong, L Shu, T Wang… - Sensors and Actuators A …, 2024 - Elsevier
Electroencephalography is valued in brain research for its high temporal resolution and user-
friendly nature. However, limitations in spatial resolution and susceptibility to biological …

Noninvasive characterization of functional pathways in layer-specific microcircuits of the human brain using 7T fMRI

G Deshpande, Y Wang - Brain Sciences, 2022 - mdpi.com
Layer-specific cortical microcircuits have been explored through invasive animal studies, yet
it is not possible to reliably characterize them functionally and noninvasively in the human …