A Comparative Study of Subject-Dependent and Subject-Independent Strategies for EEG-Based Emotion Recognition using LSTM Network

Nath, Debarshi; Anubhav,; Singh, Mrigank; Sethia, Divyashikha; Indu, S.

doi:10.1145/3388142.3388167

Cited by 29 publications

(11 citation statements)

References 11 publications

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“…of five was considered to separate the low and high valence classes as commonly performed in many other works such as Refs. [ 58 , 60 , 61 , 73 , 74 , 76 , 77 , 91 , 92 , 93 , 94 ]. This threshold value is typically chosen to overcome the class imbalance issue in the DEAP dataset [ 64 , 67 ].…”

Section: Methodsmentioning

confidence: 99%

“…EEG emotion recognition approaches can be divided into subject-dependent and subject-independent [ 46 , 73 ]. Subject-dependent methods train a separate model for each subject within the dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such results are mainly due to the discrepancy between subjects related to how they feel and express their emotions [ 75 ]. For example, Nath et al [ 73 ] have observed that EEG signals from a specific subject were somewhat similar yet significantly varied across different subjects, even when the same stimulus was considered. In addition, Putra et al [ 75 ] found that different subjects varied in their response to valence stimuli, with some subjects being more responsive than others [ 75 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abdel-Hamid

2023

Sensors

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Emotion artificial intelligence (AI) is being increasingly adopted in several industries such as healthcare and education. Facial expressions and tone of speech have been previously considered for emotion recognition, yet they have the drawback of being easily manipulated by subjects to mask their true emotions. Electroencephalography (EEG) has emerged as a reliable and cost-effective method to detect true human emotions. Recently, huge research effort has been put to develop efficient wearable EEG devices to be used by consumers in out of the lab scenarios. In this work, a subject-dependent emotional valence recognition method is implemented that is intended for utilization in emotion AI applications. Time and frequency features were computed from a single time series derived from the Fp1 and Fp2 channels. Several analyses were performed on the strongest valence emotions to determine the most relevant features, frequency bands, and EEG timeslots using the benchmark DEAP dataset. Binary classification experiments resulted in an accuracy of 97.42% using the alpha band, by that outperforming several approaches from literature by ~3–22%. Multiclass classification gave an accuracy of 95.0%. Feature computation and classification required less than 0.1 s. The proposed method thus has the advantage of reduced computational complexity as, unlike most methods in the literature, only two EEG channels were considered. In addition, minimal features concluded from the thorough analyses conducted in this study were used to achieve state-of-the-art performance. The implemented EEG emotion recognition method thus has the merits of being reliable and easily reproducible, making it well-suited for wearable EEG devices.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Abdel-Hamid

2023

Sensors

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“…Experimental results on the benchmark emotion datasets of EEG and facial expression show that the proposed method is significantly better than those state-of-the-art methods. Nath et al (2020) compared the emotion recognition effects of LSTM with KNN, SVM, DT, and RF. Among them, LSTM has the best robustness and accuracy.…”

Section: Deep Learning For Eeg Emotion Recognitionmentioning

confidence: 99%

Deep learning-based EEG emotion recognition: Current trends and future perspectives

et al. 2023

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Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human–computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.

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“…Most researches only consider one of the features in the time-domain and frequency-domain, which will lead to insufficient feature extraction of EEG signal and the problem of low recognition accuracy. Only a few studies achieve high accuracy by fully combining time-domain and frequency-domain features [16]. For image and signal data, good data pre-processing can effectively reduce noise data, thus effectively improving accuracy [8].…”

Section: Introductionmentioning

confidence: 99%

GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

Jian

Zhang

Qiu

et al. 2021

Preprint

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With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

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A Comparative Study of Subject-Dependent and Subject-Independent Strategies for EEG-Based Emotion Recognition using LSTM Network

Cited by 29 publications

References 11 publications

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

An Efficient Machine Learning-Based Emotional Valence Recognition Approach Towards Wearable EEG

Deep learning-based EEG emotion recognition: Current trends and future perspectives

GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

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