Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals

Ramzan, Munaza; Dawn, Suma

doi:10.1080/00207454.2021.1941947

Cited by 45 publications

(17 citation statements)

References 22 publications

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“…As Table 6 and Figure 5 shown, the training parameters of CNN-RNN without the channel-temporal attention mechanism (264,707/532,211) were much smaller than those of RNN (544,243/1108,963), while the average accuracy was substantially higher than that of RNN (19.34% and 21.29% improvement). This, as has been shown in previous studies ( Sheykhivand et al, 2020 ; Zhang et al, 2020 ; Ramzan and Dawn, 2021 ), demonstrates that it is necessary to consider both spatial and temporal information of EEG signals for emotion recognition. And the CA-CNN-RNN models achieved an average accuracy of 78.11% (1.56% lower than CNN-LSTM model and 10% improvement on negative emotion) and 91.11% (3.37% improvement over CNN-Bi-LSTM model), respectively.…”

Section: Discussionsupporting

confidence: 83%

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults

Jiang

Siriaraya

Choi

et al. 2022

Front. Aging Neurosci.

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Reminiscence and conversation between older adults and younger volunteers using past photographs are very effective in improving the emotional state of older adults and alleviating depression. However, we need to evaluate the emotional state of the older adult while conversing on the past photographs. While electroencephalogram (EEG) has a significantly stronger association with emotion than other physiological signals, the challenge is to eliminate muscle artifacts in the EEG during speech as well as to reduce the number of dry electrodes to improve user comfort while maintaining high emotion recognition accuracy. Therefore, we proposed the CTA-CNN-Bi-LSTM emotion recognition framework. EEG signals of eight channels (P3, P4, F3, F4, F7, F8, T7, and T8) were first implemented in the MEMD-CCA method on three brain regions separately (Frontal, Temporal, Parietal) to remove the muscle artifacts then were fed into the Channel-Temporal attention module to get the weights of channels and temporal points most relevant to the positive, negative and neutral emotions to recode the EEG data. A Convolutional Neural Networks (CNNs) module then extracted the spatial information in the new EEG data to obtain the spatial feature maps which were then sequentially inputted into a Bi-LSTM module to learn the bi-directional temporal information for emotion recognition. Finally, we designed four group experiments to demonstrate that the proposed CTA-CNN-Bi-LSTM framework outperforms the previous works. And the highest average recognition accuracy of the positive, negative, and neutral emotions achieved 98.75%.

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Section: Discussionsupporting

confidence: 83%

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults

Jiang

Siriaraya

Choi

et al. 2022

Front. Aging Neurosci.

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“…This culminated in an average accuracy of 94.13%. Ramzan et al [29] combined CNN and LSTM-RNN; two deep learning models appeared to work better when analyzing EEG signals for emotions. The fused deep learning classification model's analysis of the SEED dataset's average accuracy in detecting both positive and negative emotions yielded a result of 93.74%.…”

Section: Related Workmentioning

confidence: 99%

“…Optimization entails the pursuit of an optimal parameter configuration that empowers the model to yield precise predictions on training data and extend its effectiveness to new, unobserved data. In our research, the Adam optimizer will be used because of it is efficiency compared to other optimizers [18,28,29].…”

Section: Classificationmentioning

confidence: 99%

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A CNN Approach for Emotion Recognition via EEG

Mahmoud,

Amin,

Al Rahhal

et al. 2023

Symmetry

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Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural networks (CNNs) for EEG emotion recognition. Unlike traditional methods, our CNN-based approach learns discriminative cues directly from raw EEG signals, bypassing the need for intricate feature engineering. This approach not only simplifies the preprocessing pipeline but also allows for the extraction of more informative features. We achieve state-of-the-art performance on benchmark emotion datasets, namely DEAP and SEED datasets, showcasing the superiority of our approach in capturing subtle emotional cues. In particular, accuracies of 96.32% and 92.54% were achieved on SEED and DEAP datasets, respectively. Further, our pipeline is robust against noise and artefact interference, enhancing its applicability in real-world scenarios.

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“…With the development of technology, deep learning techniques were also heavily applied to EEG signal emotion recognition [ [19] , [20] , [21] ]. Zhong P et al [ 22 ] proposed a method based on regularized graph neural networks (RGNN) for emotion recognition of EEG, which accuracy was 94.24 % on the SEED dataset.…”

Section: Introductionmentioning

confidence: 99%

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Zhang,

Xu,

Zhang

et al. 2024

Heliyon

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Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals

Cited by 45 publications

References 22 publications

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults

Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults

A CNN Approach for Emotion Recognition via EEG

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

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