AIMC Topic: Electroencephalography

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EEG-Based Epilepsy Recognition via Multiple Kernel Learning.

Computational and mathematical methods in medicine
In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least squares support vector machine based on multikernel learning is proposed and applied to the recognition of...

Electroencephalography Might Improve Diagnosis of Acute Stroke and Large Vessel Occlusion.

Stroke
BACKGROUND AND PURPOSE: Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electroencephalography is rapidly sensitive to brain ischemia. This study examined the diagnostic utility ...

FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network.

Sensors (Basel, Switzerland)
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocar...

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities.

Computational intelligence and neuroscience
Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing ...

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM.

Sensors (Basel, Switzerland)
Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an...

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals.

International journal of neural systems
Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencepha...

Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio.

PloS one
Association between electroencephalography (EEG) and individually personal information is being explored by the scientific community. Though person identification using EEG is an attraction among researchers, the complexity of sensing limits using su...

Pattern Recognition of Cognitive Load Using EEG and ECG Signals.

Sensors (Basel, Switzerland)
The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explor...

Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis.

Journal of neuroscience methods
BACKGROUND: Emotion recognition has been studied for decades, but the classification accuracy needs to be improved.

Which Visual Modality Is Important When Judging the Naturalness of the Agent (Artificial Versus Human Intelligence) Providing Recommendations in the Symbolic Consumption Context?

Sensors (Basel, Switzerland)
This study aimed to explore how the type and visual modality of a recommendation agent's identity affect male university students' (1) self-reported responses to agent-recommended symbolic brand in evaluating the naturalness of virtual agents, human,...