AIMC Topic: Electroencephalography

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Deep learning approaches for neural decoding across architectures and recording modalities.

Briefings in bioinformatics
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
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)
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.)
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
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...

Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Motor imagery electroencephalogram (MI-EEG) play an important role in the field of neurorehabilitation, and a fuzzy support vector machine (FSVM) is one of the most used classifiers. Specifically, a fuzzy c-means (FCM) algorithm was used ...

Implementation of an SSVEP-based intelligent home service robot system.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: People with severe neuromuscular disorders caused by an accident or congenital disease cannot normally interact with the physical environment. The intelligent robot technology offers the possibility to solve this problem. However, the rob...

[Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a r...

A deep learning-based algorithm for detection of cortical arousal during sleep.

Sleep
STUDY OBJECTIVES: The frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep test...

Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.

Sleep
STUDY OBJECTIVES: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.