A novel explainable machine learning approach for EEG-based brain-computer interface systems
Electroencephalographic (EEG) recordings can be of great help in decoding the open/close
hand's motion preparation. To this end, cortical EEG source signals in the motor cortex …
hand's motion preparation. To this end, cortical EEG source signals in the motor cortex …
Emotion recognition using spatial-temporal EEG features through convolutional graph attention network
Objective. Constructing an efficient human emotion recognition model based on
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …
Real-time artifacts reduction during TMS-EEG co-registration: a comprehensive review on technologies and procedures
Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity
can be simultaneously recorded using electroencephalography (EEG). However, TMS …
can be simultaneously recorded using electroencephalography (EEG). However, TMS …
GFIL: A unified framework for the importance analysis of features, frequency bands, and channels in EEG-based emotion recognition
Accurately and automatically recognizing the emotional states of human beings is the
central task in affective computing. The electroencephalography (EEG) data, generated from …
central task in affective computing. The electroencephalography (EEG) data, generated from …
Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy
W Li, Z Zhang, A Song - Measurement, 2021 - Elsevier
Exploration on emotions continues from past to present. Nowadays, with the rapid
advancement of intelligent technology, computer-aided emotion recognition using …
advancement of intelligent technology, computer-aided emotion recognition using …
EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition
Electroencephalography (EEG) signals can demonstrate the activities of the human brain
and recognize different emotional states. Emotion recognition based on full EEG channels …
and recognize different emotional states. Emotion recognition based on full EEG channels …
Automated accurate emotion classification using Clefia pattern-based features with EEG signals
Background: The electroencephalogram (EEG) emotion classification/recognition is one of
the popular issues for advanced signal classification. However, it is difficult to manually …
the popular issues for advanced signal classification. However, it is difficult to manually …
Novel high-dimensional phase space features for EEG emotion recognition
Currently, a fundamental role of emotion recognition is apparent in both medical and non-
medical applications. The current work envisioned providing novel procedures to …
medical applications. The current work envisioned providing novel procedures to …
EEG-based emotion recognition of deaf subjects by integrated genetic firefly algorithm
Z Tian, D Li, Y Song, Q Gao, Q Kang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In recent years, many researchers have explored different methods to obtain discriminative
features for electroencephalogram-based (EEG-based) emotion recognition, but a few …
features for electroencephalogram-based (EEG-based) emotion recognition, but a few …
Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition
Numerous previous studies have proved the enormous potential of high frequency EEG in
emotion recognition, however, the current existing EEG analytic methods are not so effective …
emotion recognition, however, the current existing EEG analytic methods are not so effective …