AIMC Topic: Brain

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Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.

Scientific reports
Many studies report predictions for cognitive function but there are few predictions in epileptic patients; therefore, we established a workflow to efficiently predict outcomes of both the Mini-Mental State Examination (MMSE) and Montreal Cognitive A...

SMBFT: A Modified Fuzzy -Means Algorithm for Superpixel Generation.

Computational and mathematical methods in medicine
Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classifi...

Predicting Human Intention-Behavior Through EEG Signal Analysis Using Multi-Scale CNN.

IEEE/ACM transactions on computational biology and bioinformatics
At present, the application of Electroencephalogram (EEG) signal classification to human intention-behavior prediction has become a hot topic in the brain computer interface (BCI) research field. In recent studies, the introduction of convolutional n...

A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding.

IEEE/ACM transactions on computational biology and bioinformatics
Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN...

Subject-Independent Emotion Recognition of EEG Signals Based on Dynamic Empirical Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computin...

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection.

IEEE/ACM transactions on computational biology and bioinformatics
Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EE...

Recovering SWI-filtered phase data using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs.

In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks.

Nature materials
Neuromorphic computing aims at the realization of intelligent systems able to process information similarly to our brain. Brain-inspired computing paradigms have been implemented in crossbar arrays of memristive devices; however, this approach does n...

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.

Neuroradiology
PURPOSE: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the cli...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...