AIMC Topic: Electroencephalography

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Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

IEEE transactions on cybernetics
The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classificatio...

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

IEEE transactions on cybernetics
Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, th...

Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques.

Journal of neural engineering
Attention deficit hyperactivity disorder (ADHD) is considered one of the most common psychiatric disorders in childhood. The incidence of this disease in the community draws an increasing graph from the past to the present. While the ADHD diagnosis i...

Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model.

Physical and engineering sciences in medicine
Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective ...

Software Usability Testing Using EEG-Based Emotion Detection and Deep Learning.

Sensors (Basel, Switzerland)
It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usabilit...

Implantation accuracy and operative variables in robot-assisted stereoelectroencephalography.

Journal of neurosurgery
OBJECTIVE: The stereoelectroencephalography (SEEG) procedure provides a unique 3D overview of the seizure-onset zone. Although the success of SEEG relies on the accuracy of depth electrode implantation, few studies have investigated how different imp...

Accuracy of Depth Electrodes is Not Time-Dependent in Robot-Assisted Stereoelectroencephalography in a Pediatric Population.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVES: Robot-assisted stereoelectroencephalography (sEEG) is steadily supplanting traditional frameless and frame-based modalities for minimally invasive depth electrode placement in epilepsy workup. Accuracy rates similar to gold...

Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages.

IEEE transactions on bio-medical engineering
Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study,...

Identification of attention deficit hyperactivity disorder with deep learning model.

Physical and engineering sciences in medicine
This article explores the detection of Attention Deficit Hyperactivity Disorder, a neurobehavioral disorder, from electroencephalography signals. Due to the unstable behavior of electroencephalography signals caused by complex neuronal activity in th...