Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition

Dura, Aleksandra; Wosiak, Agnieszka; Stasiak, Bartłomiej; Wojciechowski, Adam; Rogowski, Jan

doi:10.1007/978-3-030-77967-2_44

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…The average accuracy of CTA-RNN/CTA-CNN-RNN not only increased but also achieved the minimum variance, demonstrating that the temporal attention mechanism did improve the representation of emotional state change time points in EEG signals while further suppressing the noise/artifact information. And the results were higher than the accuracy results of the previously mentioned related studies using channel selection(Alotaiby et al, 2015;Tong et al, 2018;Tao et al, 2020;Dura et al, 2021). Therefore, the EEG raw data was processed by the channel-temporal attention module to emphasize meaningful feature information and suppress irrelevant information in both channel and temporal dimensions.In the second and third modules of our proposed framework, the recoded EEG signals (containing information on the most relevant channel and temporal dimensions to the task) from the channel-temporal attention module were fed into the CNNs and RNN to extract spatial and temporal features for emotion recognition.…”

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confidence: 82%

“…At the cost of losing 1.6% accuracy, 13 channels with the highest contribution to emotion classification under time-domain features were selected from the initial 32 channels. Later, a study ( Dura et al, 2021 ) used the reverse correlation algorithm applied to the band-time-domain features of 32 channels to construct a subset of electrodes with the smallest band correlation for each subject. The number of occurrences of each subset in each subject was then calculated to obtain the most common subset of channels.…”

Section: Introductionmentioning

confidence: 99%

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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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confidence: 82%

Section: Introductionmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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“…This was motivated by the authors because, as highlighted by other studies, the right-brain activity reflects a negative emotional state and negative emotions are considered to elicit more reactivity with respect to positive ones. Similarly, in [136], and [137] sets of respectively 11 and 4 optimal channels are found for the emotion classification problem on the DEAP dataset. To generalise the channel selection to an unseen subject, in [136], [137] the RCA is adopted for each subject.…”

Section: Device Positionsmentioning

confidence: 99%

“…Similarly, in [136], and [137] sets of respectively 11 and 4 optimal channels are found for the emotion classification problem on the DEAP dataset. To generalise the channel selection to an unseen subject, in [136], [137] the RCA is adopted for each subject. Then the most frequent channels are selected.…”

Section: Device Positionsmentioning

confidence: 99%

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

et al. 2022

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The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.INDEX TERMS Emotion, EEG, channel selection, machine learning, neurophysiology of emotions, wearable devices.

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EEG channel selection strategy for deep learning in emotion recognition

Dura

Wosiak

2021

Procedia Computer Science

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Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition

Cited by 5 publications

References 30 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 Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

EEG channel selection strategy for deep learning in emotion recognition

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