CNN and LSTM based ensemble learning for human emotion recognition using EEG recordings
A Iyer, SS Das, R Teotia, S Maheshwari… - Multimedia Tools and …, 2023 - Springer
Emotion is a significant parameter in daily life and is considered an important factor for
human interactions. The human-machine interactions and their advanced stages like …
human interactions. The human-machine interactions and their advanced stages like …
EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands
In recent years, automated seizure identification with electroencephalogram (EEG) signals
has received considerable attention and appears to be an appropriate approach for …
has received considerable attention and appears to be an appropriate approach for …
Multivariate fast iterative filtering based automated system for grasp motor imagery identification using EEG signals
One of the most crucial use of hands in daily life is grasping. Sometimes people with
neuromuscular disorders become incapable of moving their hands. This article proposes a …
neuromuscular disorders become incapable of moving their hands. This article proposes a …
SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring
Introduction: We propose an automatic sleep stage scoring model, referred to as
SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short …
SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short …
Enhanced multimodal emotion recognition in healthcare analytics: A deep learning based model-level fusion approach
Deep learning techniques have drawn considerable interest in emotion recognition due to
recent technological developments in healthcare analytics. Automatic patient emotion …
recent technological developments in healthcare analytics. Automatic patient emotion …
Context-based emotion recognition: A survey
Emotions play a crucial role in human communication, and accurately recognizing them is
essential for the development of intelligent systems capable of effective human interaction …
essential for the development of intelligent systems capable of effective human interaction …
A regression method for EEG-based cross-dataset fatigue detection
D Yuan, J Yue, X Xiong, Y Jiang, P Zan, C Li - Frontiers in Physiology, 2023 - frontiersin.org
Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When
faced with new datasets, the existing fatigue detection model needs a large amount of …
faced with new datasets, the existing fatigue detection model needs a large amount of …
Variational mode decomposition-based finger flexion detection using ecog signals
The finger flexion movement prediction is a challenging problem of the brain-computer
interface. This chapter focuses on decoding the finger flexion movement using …
interface. This chapter focuses on decoding the finger flexion movement using …
A robust feature adaptation approach against variation of muscle contraction forces for myoelectric pattern recognition-based gesture characterization
The lack of a robust scheme that can withstand the muscle contraction force variations
(MCFV) in pattern recognition (PR)-based myoelectric prosthesis is a major challenge that …
(MCFV) in pattern recognition (PR)-based myoelectric prosthesis is a major challenge that …
Personality analysis based on multi-characteristic EEG signals
Y Liao, R Chen, Z Li, L Jie, R Yan, M Li - Biomedical Signal Processing and …, 2025 - Elsevier
The brain-computer interface (BCI) technology possesses the potential to analyze
personality traits objectively. Nevertheless, the majority of the existing research on …
personality traits objectively. Nevertheless, the majority of the existing research on …