Seizures

Human-robot collaborative task planning using anticipatory brain responses.

Human-robot interaction (HRI) describes scenarios in which both human and robot work as partners, sharing the same environment or complementing each other on a joint task. HRI is characterized by the need for high adaptability and flexibility of robotic systems toward their human interaction partners. One of the major challenges in HRI is task planning with dynamic subtask assignment, which is par...

Effect of sugammadex on processed EEG parameters in patients undergoing robot-assisted radical prostatectomy.

BACKGROUND: Sugammadex has been associated with increases in the bispectral index (BIS). We evaluated the effects of sugammadex administration on quantitative electroencephalographic (EEG) and electromyographic (EMG) measures.

Deep learning in neuroimaging of epilepsy.

In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural netwo...

Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing.

Deep learning models have been widely used in electroencephalogram (EEG) analysis and obtained excellent performance. But the adversarial attack and d...

Characteristic analysis of epileptic brain network based on attention mechanism.

Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this paper, we developed an EEG-based multi-frequency ...

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study.

Due to the phenomenon of "involution" in China, the current generation of college and university students are experiencing escalating levels of stress...

A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and heal...

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automa...

FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, exis...

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiog...

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endu...

The clinical application of neuro-robot in the resection of epileptic foci: a novel method assisting epilepsy surgery.

During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying and resecting MRI-negative or deep-seated epilept...

Protocol of a prospective multicenter randomized controlled trial of robot-assisted stereotactic lesioning in the treatment of focal drug-resistant epilepsy.

BACKGROUND: This protocol describes the design of a multicenter randomized controlled trial of robot-assisted stereotactic lesioning versus epileptoge...

Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease...

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands an...

Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.

It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective...

Exoskeleton Training Modulates Complexity in Movement Patterns and Cortical Activity in Able-Bodied Volunteers.

Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot intera...

Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians...

Robot-assisted stereoencephalography vs subdural electrodes in the evaluation of temporal lobe epilepsy.

OBJECTIVE: Invasive video-electroencephalography (iVEEG) is the gold standard for evaluation of refractory temporal lobe epilepsy before second stage ...

Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism.

Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomark...