AIMC Topic: Electroencephalography

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Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

Journal of affective disorders
BACKGROUND: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing trea...

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

Medical & biological engineering & computing
Decision-making plays a critical role in an individual's interpersonal interactions and cognitive processes. Due to the issue of strong subjectivity in the classification research of art design decisions, we utilize the relatively objective electroen...

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

Sensors (Basel, Switzerland)
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 of safety problems and work mistakes. Examining the detection of miner fatigue is important because i...

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

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

Sensors (Basel, Switzerland)
Predicting pilots' mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which a...

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

Computer methods in biomechanics and biomedical engineering
As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for...

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

Medical engineering & physics
Cervical spondylotic myelopathy (CSM) is the most severe type of cervical spondylosis. It is challenging to achieve early diagnosis with current clinical diagnostic tools. In this paper, we propose an end-to-end deep learning approach for early diagn...

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

Computer methods in biomechanics and biomedical engineering
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, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased huma...

A Deep Learning Framework for Anesthesia Depth Prediction from Drug Infusion History.

Sensors (Basel, Switzerland)
In the target-controlled infusion (TCI) of propofol and remifentanil intravenous anesthesia, accurate prediction of the depth of anesthesia (DOA) is very challenging. Patients with different physiological characteristics have inconsistent pharmacodyn...

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

Computers in biology and medicine
OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signal...