AIMC Topic: Electroencephalography

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The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.

IEEE transactions on biomedical circuits and systems
For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Art...

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Clinical EEG and neuroscience
Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful fo...

Towards Real-Time Sleep Stage Prediction and Online Calibration Based on Architecturally Switchable Deep Learning Models.

IEEE journal of biomedical and health informatics
Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle bo...

EEG-BCI-based motor imagery classification using double attention convolutional network.

Computer methods in biomechanics and biomedical engineering
This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena observed when performing motor tasks using motor imagery (MI). The noise present in the original data,...

A robust deep learning detector for sleep spindles and K-complexes: towards population norms.

Scientific reports
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variabili...

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

Applied neuropsychology. Child
The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patterns of inattention, hyperactivity, and impulsivity, the etiology of which may involve a variety of ...

An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.

Computers in biology and medicine
This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion classification is one of the most basic approaches for improving the quality of emotion recognition syst...

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning.

Computers in biology and medicine
Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate...

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Computers in biology and medicine
Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information pl...

Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

IEEE transactions on bio-medical engineering
OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of discriminative features from high-dimensional data, such as multi-channel electroencephalography (EEG)....