AIMC Topic: Electroencephalography

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Detecting cognitive traits and occupational proficiency using EEG and statistical inference.

Scientific reports
Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a...

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

Medical & biological engineering & computing
The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, et...

A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

BMC medical informatics and decision making
INTRODUCTION: Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by...

Fast reconstruction of EEG signal compression sensing based on deep learning.

Scientific reports
When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the...

SlumberNet: deep learning classification of sleep stages using residual neural networks.

Scientific reports
Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture...

Optimized FFNN with multichannel CSP-ICA framework of EEG signal for BCI.

Computer methods in biomechanics and biomedical engineering
The electroencephalogram (EEG) of the patient is used to identify their motor intention, which is then converted into a control signal through a brain-computer interface (BCI) based on motor imagery. Whenever gathering features from EEG signals, maki...

Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.

IEEE transactions on bio-medical engineering
OBJECTIVE: In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models.

Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index.

Annals of clinical and translational neurology
OBJECTIVE: This longitudinal study investigated potential positive impact of CPAP treatment on brain health in individuals with obstructive sleep Apnea (OSA). To allow this, we aimed to employ sleep electroencephalogram (EEG)-derived brain age index ...

A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the l...

A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.

Medical & biological engineering & computing
In recent years, fatigue driving has become the main cause of traffic accidents, leading to increased attention towards fatigue detection systems. However, the pooling and strided convolutional operations in fatigue detection algorithm based on tradi...