AIMC Topic: Electroencephalography
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A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.
Scientific reports
Jul 23, 2024
Epilepsy is one of the most well-known neurological disorders globally, leading to individuals experiencing sudden seizures and significantly impacting their quality of life. Hence, there is an urgent necessity for an efficient method to detect and p...
Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.
Surgical endoscopy
Jul 22, 2024
BACKGROUND: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS) holds critical importance for both surgical education and patient safety. This study introduces machine learning (ML) techniques using features deriv...
Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.
Medical & biological engineering & computing
Jul 19, 2024
Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic sys...
Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity.
NeuroImage
Jul 19, 2024
Differential diagnosis of acute loss of consciousness (LOC) is crucial due to the need for different therapeutic strategies despite similar clinical presentations among etiologies such as nonconvulsive status epilepticus, metabolic encephalopathy, an...
Preparatory activity of anterior insula predicts conflict errors: integrating convolutional neural networks and neural mass models.
Scientific reports
Jul 19, 2024
Preparatory brain activity is a cornerstone of proactive cognitive control, a top-down process optimizing attention, perception, and inhibition, fostering cognitive flexibility and adaptive attention control in the human brain. In this study, we prop...
Feature extraction method of EEG based on wavelet packet reconstruction and deep learning model of VR motion sickness feature classification and prediction.
PloS one
Jul 19, 2024
The surging popularity of virtual reality (VR) technology raises concerns about VR-induced motion sickness, linked to discomfort and nausea in simulated environments. Our method involves in-depth analysis of EEG data and user feedback to train a soph...
The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 19, 2024
In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the exist...
A novel deep learning model based on transformer and cross modality attention for classification of sleep stages.
Journal of biomedical informatics
Jul 18, 2024
The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspecti...
A machine learning artefact detection method for single-channel infant event-related potential studies.
Journal of neural engineering
Jul 16, 2024
. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in neonates will improve the reproducibility and speed of analysis in clinical research compared with manual identification of artefact. Some studies use...
Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach.
Ageing research reviews
Jul 14, 2024
INTRODUCTION: Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seem...
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