AIMC Topic: Electroencephalography

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Real-time noise cancellation with deep learning.

PloS one
Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so ...

NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.

IEEE transactions on cybernetics
Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI relate...

Detection of K-complexes in EEG waveform images using faster R-CNN and deep transfer learning.

BMC medical informatics and decision making
BACKGROUND: The electroencephalography (EEG) signal carries important information about the electrical activity of the brain, which may reveal many pathologies. This information is carried in certain waveforms and events, one of which is the K-comple...

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task.

Computers in biology and medicine
Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has been employed to assess neuroelectric activity during surgical performance. Machine learning (ML) has b...

Novel 3D video action recognition deep learning approach for near real time epileptic seizure classification.

Scientific reports
Seizure semiology is a well-established method to classify epileptic seizure types, but requires a significant amount of resources as long-term Video-EEG monitoring needs to be visually analyzed. Therefore, computer vision based diagnosis support too...

Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.

IEEE journal of biomedical and health informatics
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortic...

3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition.

IEEE journal of biomedical and health informatics
Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional ...

Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.

PloS one
We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different textures were randomly applied, and they were divided into two classes, "like (positive)" and "dislike (neg...

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data.

Clinical EEG and neuroscience
Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clini...

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.

Sensors (Basel, Switzerland)
Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to improve people's lives. However, most image-based emotion recognition techniques are flawed, as humans c...