AIMC Topic: Electroencephalography

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Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.

Neural networks : the official journal of the International Neural Network Society
A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representat...

Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision.

Computational intelligence and neuroscience
In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide t...

Improved Transductive Support Vector Machine for a Small Labelled Set in Motor Imagery-Based Brain-Computer Interface.

Computational intelligence and neuroscience
Long and tedious calibration time hinders the development of motor imagery- (MI-) based brain-computer interface (BCI). To tackle this problem, we use a limited labelled set and a relatively large unlabelled set from the same subject for training bas...

Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD.

Journal of neural engineering
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders. Studies have tried to find the neural correlations of ADHD with electroencephalography (EEG). Due to the heterogeneity in the ADHD popu...

Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, theref...

Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients.

Artificial intelligence in medicine
Individuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speaking impairment and totally make the person in to locked in state (LIS). To overcome the miserable co...

Predicting individual decision-making responses based on single-trial EEG.

NeuroImage
Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). W...

A Multi-Column CNN Model for Emotion Recognition from EEG Signals.

Sensors (Basel, Switzerland)
We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a s...