AIMC Topic: Electroencephalography
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Hierarchical Learning of Statistical Regularities over Multiple Timescales of Sound Sequence Processing: A Dynamic Causal Modeling Study.
Journal of cognitive neuroscience
Jul 1, 2021
Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change ov...
Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses.
Journal of integrative neuroscience
Jun 30, 2021
In this paper, the differences between two motor imagery tasks are captured through microstate parameters (occurrence, duration and coverage, and mean spatial correlation (Mspatcorr)) derived from a novel method based on electroencephalogram microsta...
[Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Jun 25, 2021
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactio...
Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Jun 1, 2021
STUDY OBJECTIVES: The objective of this study was to evaluate interrater reliability between manual sleep stage scoring performed in 2 European sleep centers and automatic sleep stage scoring performed by the previously validated artificial intellige...
Eye contact during joint attention with a humanoid robot modulates oscillatory brain activity.
Social cognitive and affective neuroscience
Mar 24, 2021
Eye contact established by a human partner has been shown to affect various cognitive processes of the receiver. However, little is known about humans' responses to eye contact established by a humanoid robot. Here, we aimed at examining humans' osci...
Deep learning approaches for neural decoding across architectures and recording modalities.
Briefings in bioinformatics
Mar 22, 2021
Decoding behavior, perception or cognitive state directly from neural signals is critical for brain-computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method i...
[Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Feb 25, 2021
At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and ...
Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.
Cerebral cortex (New York, N.Y. : 1991)
Jan 5, 2021
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...
A state observer for the computational network model of neural populations.
Chaos (Woodbury, N.Y.)
Jan 1, 2021
A state observer plays a vital role in the design of state feedback neuromodulation schemes used to prevent and treat neurological or psychiatric disorders. This paper aims to design a state observer to reconstruct all unmeasured states of the comput...
A Review of Automated Techniques for Assisting the Early Detection of Alzheimer's Disease with a Focus on EEG.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
In this paper, we review state-of-the-art approaches that apply signal processing (SP) and machine learning (ML) to automate the detection of Alzheimer's disease (AD) and its prodromal stages. In the first part of the document, we describe the econom...
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