AIMC Topic: Electroencephalography

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Dual-Modal Information Bottleneck Network for Seizure Detection.

International journal of neural systems
In recent years, deep learning has shown very competitive performance in seizure detection. However, most of the currently used methods either convert electroencephalogram (EEG) signals into spectral images and employ 2D-CNNs, or split the one-dimens...

An AI-Inspired Spatio-Temporal Neural Network for EEG-Based Emotional Status.

Sensors (Basel, Switzerland)
The accurate identification of the human emotional status is crucial for an efficient human-robot interaction (HRI). As such, we have witnessed extensive research efforts made in developing robust and accurate brain-computer interfacing models based ...

A novel ANN adaptive Riemannian-based kernel classification for motor imagery.

Biomedical physics & engineering express
More recently, a number of studies show the interest of the use of the Riemannian geometry in EEG classification. The idea is to exploit the EEG covariance matrices, instead of the raw EEG data, and use the Riemannian geometry to directly classify th...

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals.

Sensors (Basel, Switzerland)
This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were expos...

Emotion Detection Using Deep Normalized Attention-Based Neural Network and Modified-Random Forest.

Sensors (Basel, Switzerland)
In the contemporary world, emotion detection of humans is procuring huge scope in extensive dimensions such as bio-metric security, HCI (human-computer interaction), etc. Such emotions could be detected from various means, such as information integra...

An efficient deep learning framework for P300 evoked related potential detection in EEG signal.

Computer methods and programs in biomedicine
BACKGROUND: Incorporating the time-frequency localization properties of Gabor transform (GT), the complexity understandings of convolutional neural network (CNN), and histogram of oriented gradients (HOG) efficacy in distinguishing positive peaks can...

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Scientific reports
Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it ...

A Method of Intraoperative Registration Verification to Prevent Accuracy Errors in Robot-Assisted Stereotactic Electroencephalography Electrode Placement.

World neurosurgery
BACKGROUND: Robotic-assisted stereotactic electroencephalography (sEEG) electrode placement is increasingly common at specialized epilepsy centers. High accuracy and low complication rates are essential to realizing the benefits of sEEG surgery. The ...

The accuracy of a novel self-tapping bone fiducial marker for frameless robot-assisted stereo-electro-encephalography implantation and registration techniques.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: We aimed to evaluate the accuracy and safety of a novel self-tapping bone fiducial as a registration technique for stereoelectroencephalography (SEEG) implantation.

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals.

Sensors (Basel, Switzerland)
Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram (EEG) devices, we developed a fast and accurate sleep stage classification method in this study with...