Seizures

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics.

An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition.

Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing to the requirement for active emotion perception. To enhance the performance of electroencephalography (EEG)-based emotional HCI, this paper proposes an improved common spatial pattern combined with a channel-selection strategy (ICSPCS) for EEG-base...

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states.

Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer...

Macroscale and microcircuit dissociation of focal and generalized human epilepsies.

Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at th...

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several sho...

EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.

In recent years, with the development of brain science and biomedical engineering, as well as the rapid development of electroencephalogram (EEG) sign...

EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system.

Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed ...

Digital conversations about suicide among teenagers and adults with epilepsy: A big-data, machine learning analysis.

OBJECTIVE: Digital media conversations can provide important insight into the concerns and struggles of people with epilepsy (PWE) outside of formal c...

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning.

Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagno...

Time-resolved correspondences between deep neural network layers and EEG measurements in object processing.

The ventral visual stream is known to be organized hierarchically, where early visual areas processing simplistic features feed into higher visual are...

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classifi...

A probabilistic approach for calibration time reduction in hybrid EEG-fTCD brain-computer interfaces.

BACKGROUND: Generally, brain-computer interfaces (BCIs) require calibration before usage to ensure efficient performance. Therefore, each BCI user has...

A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual reco...

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.

Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning ...

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world...

An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard.

OBJECTIVE: To validate an artificial intelligence-based computer algorithm for detection of epileptiform EEG discharges (EDs) and subsequent identific...

EEG based Classification of Long-term Stress Using Psychological Labeling.

Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for ...

A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome.

PURPOSE: The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiology-based approach. We developed a propositional...

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coeffic...

Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals.

Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of in...