AIMC Topic: Electroencephalography

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Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition.

International journal of neural systems Apr 2, 2022
Currently, Fourier-based, wavelet-based, and Hilbert-based time-frequency techniques have generated considerable interest in classification studies for emotion recognition in human-computer interface investigations. Empirical mode decomposition (EMD)...
Humans Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted Deep Learning Emotions
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Decoding ECoG signal into 3D hand translation using deep learning.

Journal of neural engineering Mar 31, 2022
Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals wi...
Humans Deep Learning Brain-Computer Interfaces Electroencephalography Hand Electrocorticography
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Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.

Scientific reports Mar 30, 2022
In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogeni...
Deep Learning Electroencephalography Epilepsy Signal Processing, Computer-Assisted Humans Biomarkers
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EEG Feature Extraction and Data Augmentation in Emotion Recognition.

Computational intelligence and neuroscience Mar 28, 2022
Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain activities that are created due to emotional stimuli. This is one of the most substantial advantages of ...
Arousal Emotions Neural Networks, Computer Electroencephalography Support Vector Machine
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Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.

Sensors (Basel, Switzerland) Mar 25, 2022
Large-scale functional connectivity is an important indicator of the brain's normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the ...
Electroencephalography Motor Cortex Aged Artificial Intelligence Adult Brain Humans Magnetic Resonance Imaging Brain Mapping Neural Networks, Computer
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Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic.

International journal of neural systems Mar 18, 2022
Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own pr...
Fuzzy Logic Electroencephalography Humans Algorithms Seizures Signal Processing, Computer-Assisted
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Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

Journal of healthcare engineering Mar 15, 2022
OBJECTIVE: To search the predictive value of epilepsy secondary to acute subarachnoid hemorrhage (aSAH) based on EEG wave pattern in deep learning.
Hemorrhage Humans Deep Learning Scalp Epilepsy Electroencephalography Seizures
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Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features.

Artificial intelligence in medicine Mar 9, 2022
This work proposed a novel method for automatic sleep stage classification based on the time, frequency, and fractional Fourier transform (FRFT) domain features extracted from a single-channel electroencephalogram (EEG). Bidirectional long short-term...
Electroencephalography Sleep Fourier Analysis Sleep Stages Neural Networks, Computer
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Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

Journal of healthcare engineering Mar 8, 2022
The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition is becoming one of the essential research areas among various world researchers. Treatment that is ef...
Communicable Disease Control COVID-19 Emotions Algorithms Electroencephalography Humans Artificial Intelligence
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Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

Epilepsia Mar 7, 2022
OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence (AI)-based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20-min) electroencephalography (EEG) recordings.
Epilepsy Video Recording Humans Artificial Intelligence Algorithms Electroencephalography
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