Seizures

Leveraging pathological markers of lower grade glioma to predict the occurrence of secondary epilepsy, a retrospective study.

Epilepsy is a common manifestation in patients with lower grade glioma (LGG), often presenting as the initial symptom in approximately 70% of cases. This study aimed to identify clinical and pathological markers for epileptic seizures in patients with LGG. Additionally, it sought to develop and validate a machine learning model that enables tailored risk-based anti-seizure treatment. Health record...

Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicentre validation study.

BACKGROUND: Surgical resection is an effective treatment for medically refractory mesial temporal lobe epilepsy (mTLE), however, more than one-third of patients fail to achieve seizure freedom after surgery. This study aimed to evaluate preoperative individual morphometric network characteristics and develop a machine learning model to predict surgical outcome in mTLE.

An EEG-based seizure prediction model encoding brain network temporal dynamics.

EEG-based seizure prediction enables timely treatment for patients, but its performance is limited by the difficulty in effectively characterizing the...

Artificial Intelligence and Machine Learning in Neuromodulation for Epilepsy.

Recent advances in artificial intelligence (AI) and machine learning (ML) can revolutionize neuromodulation therapies for drug-resistant epilepsy. Suc...

A novel ST-GCN model based on homologous microstate for subject-independent seizure prediction.

Due to the lack of validated universal seizure markers, population-level prediction methods often exhibit limited performance. This study proposes hom...

MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).

This work aims to promote early and accurate diagnosis of Temporal Lobe Epilepsy (TLE) by developing state-of-the-art deep learning techniques, with t...

Automated posture adjustment system for immobilized patients using EEG signals.

This paper presents a Brain Computing Interface (BCI) system utilizing Electroencephalography (EEG) for human posture Identification. The proposed app...

STIED: a deep learning model for the spatiotemporal detection of focal interictal epileptiform discharges with MEG.

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patien...

Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.

Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and act...

Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.

The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impre...

Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.

Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and...

Improving EEG based brain computer interface emotion detection with EKO ALSTM model.

Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered comp...

EmoTrans attention based emotion recognition using EEG signals and facial analysis with expert validation.

Emotion recognition via EEG signals and facial analysis becomes one of the key aspects of human-computer interaction and affective computing, enabling...

Single pulse electrical stimulation of the medial thalamic surface induces narrower high gamma band activities in the sensorimotor cortex.

The human thalamus projects nerve fibers to all cortical regions and propagates epileptic activity. However, opportunities to directly record thalamic...

EEG based real time classification of consecutive two eye blinks for brain computer interface applications.

Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific app...

Identification of plasma proteins associated with seizures in epilepsy: A consensus machine learning approach.

Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for b...

Enhancing out-of-hospital cardiac arrest survival in China through the 5-minute social rescue circle implementation.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) survival in China remains critically low due to limited bystander cardiopulmonary resuscitation (CPR...

Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.

Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually n...

The impact of EEG preprocessing parameters on ultra-low-power seizure detection.

OBJECTIVE: Closed-loop neurostimulation is a promising treatment for drug-resistant focal epilepsy. A major challenge is fast and reliable seizure det...

Microstate Analysis of Resting-State EEG Signals for Classifying Tinnitus from Healthy Subjects.

Electroencephalography (EEG) is a noninvasive technique for studying brain electrophysiology with high temporal resolution. Microstate analysis exami...