AIMC Topic: Electroencephalography

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Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

IEEE transactions on neural networks and learning systems
In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on...

EEG emotion recognition using improved graph neural network with channel selection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with promising applications in healthcare areas such as autism research and emotion detection in pregnant w...

Research of intent recognition in rehabilitation robots: a systematic review.

Disability and rehabilitation. Assistive technology
PURPOSE: Rehabilitation robots with intent recognition are helping people with dysfunction to enjoy better lives. Many rehabilitation robots with intent recognition have been developed by academic institutions and commercial companies. However, there...

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG.

Sensors (Basel, Switzerland)
Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of be...

Extracting a Novel Emotional EEG Topographic Map Based on a Stacked Autoencoder Network.

Journal of healthcare engineering
Emotion recognition based on brain signals has increasingly become attractive to evaluate human's internal emotional states. Conventional emotion recognition studies focus on developing machine learning and classifiers. However, most of these methods...

Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography.

Neuropsychobiology
INTRODUCTION: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalog...

Cross-task cognitive workload recognition using a dynamic residual network with attention mechanism based on neurophysiological signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Evaluation of human cognitive workload (CW) helps improve the user experience of human-centered systems. To provide a continuous estimation of the CW, we built a CW recognizer that maps human electroencephalograms (EEGs) to ...

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.

Sensors (Basel, Switzerland)
Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that imp...

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Sensors (Basel, Switzerland)
Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed vi...

Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...