Merged LSTM Model for emotion classification using EEG signals

Garg, Anumit; Kapoor, Ashna; Bedi, Anterpreet Kaur; Sunkaria, Ramesh Kumar

doi:10.1109/icdse47409.2019.8971484

Cited by 27 publications

(14 citation statements)

References 5 publications

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“…Valence Arousal Four SOTA CDCN [39] 92.24 92.92 -MMResLSTM [40] 92.87 92.30 -PCRNN [29] 90.8 91.03 -CNNLSTM [41] 90.62 86.13 -MergedLSTM [42] 84 AAN to generate augmented samples. Then, the proposed classifier is learned on each fold for 300 epochs, and we supplement the augmented samples generated by AAN for fine-tuning of 300 epochs with the help of MTN.…”

Section: Methodsmentioning

confidence: 99%

“…To assess the overall performance, the average classification accuracies over five folds are reported. Illustrated in Table II, we first compared our proposed GANSER with five state-of-the-art studies, i.e., CDCN [39], MMResLSTM [40], PCRNN [29], CNNLSTM [41], and MergedLSTM [42], on the DEAP dataset, respect to the emotion dimensions including valence and arousal. These studies develop different network architectures and strategies for emotion recognition.…”

Section: Methodsmentioning

confidence: 99%

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GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition

Zhang¹,

Zhong²,

Liu³

2021

Preprint

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The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models-increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework can generate high-quality and high-diversity simulated EEG samples. In particular, we utilize adversarial training to learn an EEG generator and force the generated EEG signals to approximate the distribution of real samples, ensuring the quality of augmented samples. A transformation function is employed to mask parts of EEG signals and force the generator to synthesize potential EEG signals based on the remaining parts, to produce a wide variety of samples. The masking possibility during transformation is introduced as prior knowledge to guide to extract distinguishable features for simulated EEG signals and generalize the classifier to the augmented sample space. Finally, extensive experiments demonstrate our proposed method can help emotion recognition for performance gain and achieve stateof-the-art results.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition

Zhang¹,

Zhong²,

Liu³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers have used LSTMs [145] and feedforward neural networks [146] to classify different classes of behaviors, using spiking activity in animals [146] and fNIRS or fMRI measurements in humans [16,145]. LSTMs [147,148] and CNNs [149] have been used to classify emotions from EEG signals. Feedforward neural networks have been used to determine the source of a subject's attention, using EEG in humans [150,151] and spiking activity in monkeys [152].…”

Section: Othermentioning

confidence: 99%

Deep learning approaches for neural decoding across architectures and recording modalities

Livezey

Glaser

2020

Briefings in Bioinformatics

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Decoding behavior, perception or cognitive state directly from neural signals is critical for brain–computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to functional magnetic resonance imaging. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

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“…Researchers have used LSTMs [114] and feedforward neural networks [115] to classify different classes of behaviors, using spiking activity in animals [115] and fNIRS measurements in humans [114]. LSTMs [116,117] and CNNs [118] have been used to classify emotions from EEG signals. Feedforward neural networks have been used to determine the source of a subjects attention, using EEG in humans [119,120] and spiking activity in monkeys [121].…”

Section: Other Outputsmentioning

confidence: 99%

Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI

Livezey¹,

Glaser²

2020

Preprint

View full text Add to dashboard Cite

Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to EEG. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech, and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

show abstract

Merged LSTM Model for emotion classification using EEG signals

Cited by 27 publications

References 5 publications

GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition

GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition

Deep learning approaches for neural decoding across architectures and recording modalities

Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI

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