Seizures

Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition.

This paper proposes two multimodal fusion methods between brain and peripheral signals for emotion recognition. The input signals are electroencephalogram and facial expression. The stimuli are based on a subset of movie clips that correspond to four specific areas of valance-arousal emotional space (happiness, neutral, sadness, and fear). For facial expression detection, four basic emotion states...

A Functional-Genetic Scheme for Seizure Forecasting in Canine Epilepsy.

OBJECTIVE: The objective of this work is the development of an accurate seizure forecasting algorithm that considers brain's functional connectivity for electrode selection.

Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning.

Impaired confrontation naming is a common symptom of temporal lobe epilepsy (TLE). The neurobiological mechanisms underlying this impairment are poorl...

Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System.

Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG s...

Identifying sleep spindles with multichannel EEG and classification optimization.

Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroenc...

A New Method for Automatic Sleep Stage Classification.

Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to ma...

Deep learning with convolutional neural networks for EEG decoding and visualization.

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning fro...

Mixed Neural Network Approach for Temporal Sleep Stage Classification.

This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage class...

Phenomenological network models: Lessons for epilepsy surgery.

The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with re...

Human interaction with robotic systems: performance and workload evaluations.

We first tested the effect of differing tactile informational forms (i.e. directional cues vs. static cues vs. dynamic cues) on objective performance ...

Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

PURPOSE: Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-in...

Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among e...

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and...

Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy: A computational study.

OBJECTIVE: In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding ...

Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of...

Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG.

A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exog...

A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems.

EEG is a standard non-invasive technique used in neural disease diagnostics and neurosciences. Frequency-tagging is an increasingly popular experiment...

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroenceph...

Unsupervised Learning of Spike Patterns for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic ( $\mu $ ECoG) Data.

For the past few years, we have developed flexible, active, and multiplexed recording devices for high resolution recording over large, clinically rel...