AIMC Topic: Electroencephalography

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Identification and diagnosis of schizophrenia based on multichannel EEG and CNN deep learning model.

Schizophrenia research Jul 14, 2024
This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training...
Electroencephalography Signal Processing, Computer-Assisted Schizophrenia Adult Male Deep Learning Humans Neural Networks, Computer Female Young Adult
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Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.

International journal of neural systems Jul 13, 2024
Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to impr...
Humans Wavelet Analysis Memory, Long-Term Electroencephalography Seizures Memory, Short-Term Neural Networks, Computer Sensitivity and Specificity Models, Neurological
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Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 11, 2024
The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity betwee...
Adult Brain Humans Multimodal Imaging Neural Networks, Computer Female Male Electroencephalography Young Adult Algorithms Deep Learning Magnetoencephalography
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A Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 11, 2024
The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS)...
Reproducibility of Results Male Least-Squares Analysis Humans Evoked Potentials, Visual Electroencephalography Machine Learning Nonlinear Dynamics Algorithms Brain-Computer Interfaces
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LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

IEEE transactions on neural networks and learning systems Jul 8, 2024
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...
Attention Humans Machine Learning Brain-Computer Interfaces Neural Networks, Computer Cognition Algorithms Emotions Brain Electroencephalography
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Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.

IEEE transactions on neural networks and learning systems Jul 8, 2024
Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity patte...
Humans Emotions Neural Networks, Computer Electroencephalography Algorithms Brain-Computer Interfaces Brain
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An Identification Method for Road Hypnosis Based on Human EEG Data.

Sensors (Basel, Switzerland) Jul 6, 2024
The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and canno...
Humans Accidents, Traffic Neural Networks, Computer Automobile Driving Electroencephalography Hypnosis
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Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches.

Medical engineering & physics Jul 5, 2024
Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal...
Epilepsy Seizures Signal Processing, Computer-Assisted Humans Child Male Diagnosis, Computer-Assisted Automation Support Vector Machine Deep Learning Entropy Electroencephalography Child, Preschool
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An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.

Journal of neural engineering Jul 5, 2024
Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with...
Humans Neural Networks, Computer Female Evoked Potentials Time Factors Young Adult Electroencephalography Adult Brain-Computer Interfaces Male
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Temporal-spatial cross attention network for recognizing imagined characters.

Scientific reports Jul 4, 2024
Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features ...
Deep Learning Electroencephalography Signal Processing, Computer-Assisted Humans Neural Networks, Computer Brain Attention Brain-Computer Interfaces Imagination
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