AIMC Topic: Electroencephalography

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Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning.

Computer methods and programs in biomedicine Jan 7, 2022
BACKGROUND AND OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requir...
Algorithms Deep Learning Brain-Computer Interfaces Evoked Potentials Electroencephalography Humans Neural Networks, Computer
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Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning.

PloS one Jan 7, 2022
OBJECTIVE: The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combi...
Humans Male Middle Aged Electroencephalography Evoked Potentials Parkinson Disease Machine Learning Brain Female Aged
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Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Jan 6, 2022
OBJECTIVE: To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity.
Support Vector Machine Electroencephalography Seizures Epilepsy Male Middle Aged Adult Sensitivity and Specificity Humans Female
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Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features.

International journal of environmental research and public health Jan 6, 2022
This paper puts forward a new method of landscape recognition and evaluation by using aerial video and EEG technology. In this study, seven typical landscape types (forest, wetland, grassland, desert, water, farmland, and city) were selected. Differe...
Humans Electroencephalography Perception Support Vector Machine Emotions Neural Networks, Computer Algorithms
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A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface.

Journal of neural engineering Jan 6, 2022
Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor ima...
Machine Learning Algorithms Humans Neural Networks, Computer Movement Imagination Sensorimotor Cortex Electroencephalography Brain-Computer Interfaces
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Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

NeuroImage Jan 5, 2022
This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within som...
Functional Neuroimaging Emotions Humans Adult Cerebral Cortex Imagination Unsupervised Machine Learning Electroencephalography
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A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification.

Biosensors Jan 3, 2022
Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography ...
Brain-Computer Interfaces Imagination Algorithms Electroencephalography Neural Networks, Computer
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Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson's disease.

Journal of neural engineering Dec 24, 2021
Parkinson's disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagnosis of early PD has always been a difficult clinical problem due to the lack of reliable biomarkers....
Humans Neural Networks, Computer Electroencephalography Deep Learning Parkinson Disease
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Decoding Color Visual Working Memory from EEG Signals Using Graph Convolutional Neural Networks.

International journal of neural systems Dec 11, 2021
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed ...
Humans Memory, Short-Term Visual Perception Brain Neural Networks, Computer Electroencephalography
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Learning Spatial-Spectral-Temporal EEG Representations with Deep Attentive-Recurrent-Convolutional Neural Networks for Pain Intensity Assessment.

Neuroscience Nov 26, 2021
Electroencephalogram (EEG)-based quantitative pain measurement is valuable in the field of clinical pain treatment, providing objective pain intensity assessment especially for nonverbal patients who are unable to self-report. At present, a key chall...
Attention Pain Measurement Electroencephalography Humans Neural Networks, Computer
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