AIMC Topic: Electroencephalography

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A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection.

Physiological measurement
UNLABELLED: Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patients, the detection of epileptic seizures is of great significance.

Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learning hierarchical feature representation automatically from raw EEG data have been proposed. However, ...

A deep learning framework for automatic diagnosis of unipolar depression.

International journal of medical informatics
BACKGROUND AND PURPOSE: In recent years, the development of machine learning (ML) frameworks for automatic diagnosis of unipolar depression has escalated to a next level of deep learning frameworks. However, this idea needs further validation. Theref...

Post-hoc modification of linear models: Combining machine learning with domain information to make solid inferences from noisy data.

NeuroImage
Linear machine learning models "learn" a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the de...

Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlatio...

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.

Biomedical journal
BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is a...

Deep learning and deep knowledge representation in Spiking Neural Networks for Brain-Computer Interfaces.

Neural networks : the official journal of the International Neural Network Society
OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, ...

A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.

PloS one
Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which ...

A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns.

IEEE journal of biomedical and health informatics
OBJECTIVE: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently...

Visual Evoked Response Modulation Occurs in a Complementary Manner Under Dynamic Circuit Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The steady-state visual-evoked potential (SSVEP) induced by the periodic visual stimulus plays an important role in vision research. An increasing number of studies use the SSVEP to manipulate intrinsic oscillation and further regulate test performan...