Seizures

Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks.

Error-related potentials are considered an important neuro-correlate for monitoring human intentionality in decision-making, human-human, or human-machine interaction scenarios. Multiple methods have been proposed in order to improve the recognition of human intentions. Moreover, current brain-computer interfaces are limited in the identification of human errors by manual tuning of parameters (e.g...

DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification.

We present in this paper an efficient convolutional neural network (CNN) running on time-frequency image features for automatic sleep stage classification. Opposing to deep architectures which have been used for the task, the proposed CNN is much simpler However, the CNN's convolutional layer is able to support convolutional kernels with different sizes, and therefore, capable of learning features...

A Two Stage Approach for the Automatic Detection of Insomnia.

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exis...

Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks.

We propose in this work a feature learning approach using deep bidirectional recurrent neural networks (RNNs) with attention mechanism for single-chan...

Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks.

Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled pe...

Convolutional Neural Network for Target Face Detection using Single-trial EEG Signal.

Face recognition plays an import role in our daily lives. However, computer face recognition performance degrades dramatically with the presence of va...

Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection.

We present for the first time a μW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22...

Deep Learning Enabled Automatic Abnormal EEG Identification.

In hospitals, physicians diagnose brain-related disorders such as epilepsy by analyzing electroencephalograms (EEG). However, manual analysis of EEG d...

Ensemble learning based on overlapping clusters of subjects to predict microsleep states from EEG.

Microsleeps are brief and involuntary instances of complete loss of sleep-related consciousness. We present a novel approach of creating overlapping c...

Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance.

This study presents a novel, deep, fully convolutional architecture which is optimized for the task of EEG-based neonatal seizure detection. Architect...

The combined use of virtual reality and EEG to study language processing in naturalistic environments.

When we comprehend language, we often do this in rich settings where we can use many cues to understand what someone is saying. However, it has tradit...

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comp...

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep exp...

Robust Support Matrix Machine for Single Trial EEG Classification.

Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important ...

As above, so below? Towards understanding inverse models in BCI.

OBJECTIVE: In brain-computer interfaces (BCI), measurements of the user's brain activity are classified into commands for the computer. With EEG-based...

VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high...

Research: Use of Dry Electroencephalogram and Support Vector for Objective Pain Assessment.

The reliability of normal gel-based electrode electroencephalogram (EEG) for measuring pain has been validated. To date, however, few documented trial...

Classification of single-channel EEG signals for epileptic seizures detection based on hybrid features.

BACKGROUND: Epilepsy is a common chronic neurological disorder of the brain. Clinically, epileptic seizures are usually detected via the continuous mo...

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However...

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features.

OBJECTIVE: Biological and non-biological artifacts cause severe problems when dealing with electroencephalogram (EEG) recordings. Independent componen...