AIMC Topic: Electroencephalography

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Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals.

Computer methods and programs in biomedicine Jan 13, 2023
BACKGROUND AND OBJECTIVE: Evaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a continuous estimation of the CW, we built a CW recognizer that maps human electroencephalograms (EEGs) to ...
Neural Networks, Computer Humans Cognition Databases, Factual Electroencephalography Workload
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Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Sensors (Basel, Switzerland) Jan 12, 2023
Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that imp...
Tinnitus Treatment Outcome Electroencephalography Artificial Intelligence Deep Learning Humans
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Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Sensors (Basel, Switzerland) Jan 9, 2023
Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed vi...
Fourier Analysis Electroencephalography Random Forest Humans Neural Networks, Computer Support Vector Machine
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Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience Jan 5, 2023
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...
Brain-Computer Interfaces Imagination Neural Networks, Computer Humans Brain Electroencephalography Algorithms Signal Processing, Computer-Assisted Discriminant Analysis
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Dual-Modal Information Bottleneck Network for Seizure Detection.

International journal of neural systems Jan 5, 2023
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimens...
Humans Neural Networks, Computer Seizures Signal Processing, Computer-Assisted Deep Learning Electroencephalography
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An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.

Sensors (Basel, Switzerland) Jan 2, 2023
The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based ...
Humans Emotions Electroencephalography Neural Networks, Computer Algorithms Recognition, Psychology
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A novel ANN adaptive Riemannian-based kernel classification for motor imagery.

Biomedical physics & engineering express Dec 30, 2022
More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify th...
Electroencephalography Imagery, Psychotherapy Brain-Computer Interfaces Neural Networks, Computer
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Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals.

Sensors (Basel, Switzerland) Dec 27, 2022
This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were expos...
Hand Electromyography Robotics Humans Movement Electroencephalography Upper Extremity
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Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.

Sensors (Basel, Switzerland) Dec 26, 2022
In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human-computer interaction), etc. Such emotions could be detected from various means, such as information integra...
Artificial Intelligence Algorithms Humans Neural Networks, Computer Emotions Random Forest Electroencephalography
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An efficient deep learning framework for P300 evoked related potential detection in EEG signal.

Computer methods and programs in biomedicine Dec 25, 2022
BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can...
Electroencephalography Neural Networks, Computer Algorithms Brain-Computer Interfaces Evoked Potentials Deep Learning
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