A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Brain signals refer to the biometric information collected from the human brain. The research
on brain signals aims to discover the underlying neurological or physical status of the …
on brain signals aims to discover the underlying neurological or physical status of the …
[PDF][PDF] A survey on deep learning based brain computer interface: Recent advances and new frontiers
Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …
by decoding individuals' brain signals into commands recognizable by computer devices …
EEG-based emotion recognition using regularized graph neural networks
Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
Emotionmeter: A multimodal framework for recognizing human emotions
In this paper, we present a multimodal emotion recognition framework called EmotionMeter
that combines brain waves and eye movements. To increase the feasibility and wearability …
that combines brain waves and eye movements. To increase the feasibility and wearability …
Identifying stable patterns over time for emotion recognition from EEG
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for
emotion recognition using a machine learning approach. Up to now, various findings of …
emotion recognition using a machine learning approach. Up to now, various findings of …
Multisource transfer learning for cross-subject EEG emotion recognition
Electroencephalogram (EEG) has been widely used in emotion recognition due to its high
temporal resolution and reliability. Since the individual differences of EEG are large, the …
temporal resolution and reliability. Since the individual differences of EEG are large, the …
Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition
EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …
[HTML][HTML] Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition
Y Cimtay, E Ekmekcioglu - Sensors, 2020 - mdpi.com
The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …
its resistance to deceptive actions of humans. This is one of the most significant advantages …
Self‐training maximum classifier discrepancy for EEG emotion recognition
X Zhang, D Huang, H Li, Y Zhang… - CAAI Transactions on …, 2023 - Wiley Online Library
Even with an unprecedented breakthrough of deep learning in electroencephalography
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …
Domain adaptation techniques for EEG-based emotion recognition: a comparative study on two public datasets
Affective brain-computer interface (aBCI) introduces personal affective factors to human-
computer interaction. The state-of-the-art aBCI tailors its classifier to each individual user to …
computer interaction. The state-of-the-art aBCI tailors its classifier to each individual user to …