Seizures

Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer ...

EESCN: A novel spiking neural network method for EEG-based emotion recognition.

BACKGROUND AND OBJECTIVE: Although existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG.

Oscillatory Responses to Tactile Stimuli of Different Intensity.

Tactile perception encompasses several submodalities that are realized with distinct sensory subsystems. The processing of those submodalities and the...

Spatio-Temporal Explanation of 3D-EEGNet for Motor Imagery EEG Classification Using Permutation and Saliency.

Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces ...

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection.

To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggrega...

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection.

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain e...

Space-CNN: a decision classification method based on EEG signals from different brain regions.

Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes. Due to the issue of strong subjectivity i...

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network.

Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk o...

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

Predicting pilots' mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenu...

IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algo...

Aided diagnosis of cervical spondylotic myelopathy using deep learning methods based on electroencephalography.

Cervical spondylotic myelopathy (CSM) is the most severe type of cervical spondylosis. It is challenging to achieve early diagnosis with current clini...

A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate.

In today's world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brai...

ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled p...

Bridging Neuroscience and Robotics: Spiking Neural Networks in Action.

Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in ...

Menthol Dissolved in Dimethyl Sulfoxide Protects Against Epileptiform Activity Induced by Pentylenetetrazol in Male Rats.

INTRODUCTION: This research aims to investigate the protective action of menthol dissolved in dimethyl sulfoxide (DMSO) on experimental epileptiform a...

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physica...

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the leve...

An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.

Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp ele...

Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data.

A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cogniti...

Natural language processing for identification of refractory status epilepticus in children.

OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic hea...