A survey on denoising techniques of electroencephalogram signals using wavelet transform
M Grobbelaar, S Phadikar, E Ghaderpour, AF Struck… - Signals, 2022 - mdpi.com
Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle
movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the …
movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the …
Deep unsupervised domain adaptation with time series sensor data: A survey
Sensors are devices that output signals for sensing physical phenomena and are widely
used in all aspects of our social production activities. The continuous recording of physical …
used in all aspects of our social production activities. The continuous recording of physical …
Explainable artificial intelligence model for stroke prediction using EEG signal
State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence
(AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are …
(AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are …
Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis
Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-
computer fusion. EEG signals have inherent temporal and spatial characteristics. However …
computer fusion. EEG signals have inherent temporal and spatial characteristics. However …
A novel baseline removal paradigm for subject-independent features in emotion classification using EEG
Emotion plays a vital role in understanding the affective state of mind of an individual. In
recent years, emotion classification using electroencephalogram (EEG) has emerged as a …
recent years, emotion classification using electroencephalogram (EEG) has emerged as a …
A survey on EEG data analysis software
Electroencephalography (EEG) is a mechanism to understand the brain's functioning by
analyzing brain electrical signals. More recently, it has been more commonly used in studies …
analyzing brain electrical signals. More recently, it has been more commonly used in studies …
Lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features
In the present era, cancer is the leading cause of demise in both men and women
worldwide, with low survival rates due to inefficient diagnostic techniques. Recently …
worldwide, with low survival rates due to inefficient diagnostic techniques. Recently …
Epileptic seizure detection based on variational mode decomposition and deep forest using EEG signals
Electroencephalography (EEG) records the electrical activity of the brain, which is an
important tool for the automatic detection of epileptic seizures. It is certainly a very heavy …
important tool for the automatic detection of epileptic seizures. It is certainly a very heavy …
ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition
Electroencephalography (EEG) emotion recognition is an important task for brain–computer
interfaces. The time, frequency, and spatial domains of EEG signals have been widely …
interfaces. The time, frequency, and spatial domains of EEG signals have been widely …
Detection of emotion by text analysis using machine learning
K Machová, M Szabóova, J Paralič, J Mičko - Frontiers in Psychology, 2023 - frontiersin.org
Emotions are an integral part of human life. We know many different definitions of emotions.
They are most often defined as a complex pattern of reactions, and they could be confused …
They are most often defined as a complex pattern of reactions, and they could be confused …