A wireless and battery-less implant for multimodal closed-loop neuromodulation in small animals
Fully implantable wireless systems for the recording and modulation of neural circuits that do
not require physical tethers or batteries allow for studies that demand the use of …
not require physical tethers or batteries allow for studies that demand the use of …
The role of the anterior nuclei of the thalamus in human memory processing
CM Sweeney-Reed, L Buentjen, J Voges… - Neuroscience & …, 2021 - Elsevier
Extensive neuroanatomical connectivity between the anterior thalamic nuclei (ATN) and
hippocampus and neocortex renders them well-placed for a role in memory processing, and …
hippocampus and neocortex renders them well-placed for a role in memory processing, and …
A seasonal-trend decomposition-based dendritic neuron model for financial time series prediction
Financial time series prediction is a hot topic in machine learning field, but existing works
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
barely catch the point of such data. In this study, we employ the most suitable preprocessing …
Schizophrenia detection using MultivariateEmpirical Mode Decomposition and entropy measures from multichannel EEG signal
PT Krishnan, ANJ Raj, P Balasubramanian… - Biocybernetics and …, 2020 - Elsevier
Multivariate analysis of the EEG signal for the detection of Schizophrenia condition is
proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose …
proposed here. Multivariate Empirical Mode Decomposition (MEMD) is used to decompose …
[HTML][HTML] EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions
Neural oscillations and their association with brain states and cognitive functions have been
object of extensive investigation over the last decades. Several electroencephalography …
object of extensive investigation over the last decades. Several electroencephalography …
Seizure onset detection using empirical mode decomposition and common spatial pattern
Automatic seizure onset detection plays an important role in epilepsy diagnosis. In this
paper, a novel seizure onset detection method is proposed by combining empirical mode …
paper, a novel seizure onset detection method is proposed by combining empirical mode …
Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification
Signal processing and machine learning methods are valuable tools in epilepsy research,
potentially assisting in diagnosis, seizure detection, prediction and real-time event detection …
potentially assisting in diagnosis, seizure detection, prediction and real-time event detection …
Changes in electrical brain activity and cognitive functions following mild to moderate COVID-19: a one-year prospective study after acute infection
P Andrei Appelt, A Taciana Sisconetto… - Clinical EEG and …, 2022 - journals.sagepub.com
The coronavirus disease 2019 (COVID-19) can disrupt various brain functions. Over a one-
year period, we aimed to assess brain activity and cognitive function in 53 COVID-19 …
year period, we aimed to assess brain activity and cognitive function in 53 COVID-19 …
Epileptic seizure classification using shifting sample difference of EEG signals
This paper proposes a novel lightweight shifting sample difference method for efficient
epileptic seizure detection from electroencephalogram signals. Unlike most recent seizure …
epileptic seizure detection from electroencephalogram signals. Unlike most recent seizure …
Discrimination of genuine and acted emotional expressions using EEG signal and machine learning
We present here one of the first studies that attempt to differentiate between genuine and
acted emotional expressions, using EEG data. We present the first EEG dataset (available …
acted emotional expressions, using EEG data. We present the first EEG dataset (available …