EEG-based emotion recognition using random Convolutional Neural Networks

Cheng, Wen; Gao, Ruobin; Suganthan, Ponnuthurai Nagaratnam; Yuen, Kum Fai

doi:10.1016/j.engappai.2022.105349

Cited by 22 publications

(8 citation statements)

References 43 publications

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“…Nevertheless, the recent approach introduced by Cheng et al [ 82 ], which is based on randomized CNN and ensemble learning, resulted in an overall accuracy of 99.17% which is 1.75% higher than the implemented method. In their work, they reported an average training time of 35.15 s. As for the proposed method, an average of 0.06 s were required for the feature computation, training, and classification.…”

Section: Discussionmentioning

confidence: 91%

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: Discussionmentioning

confidence: 91%

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

Abdel-Hamid

2023

Sensors

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“…The initial foray into EEG-based emotion classification was governed by a preliminary preprocessing phase [37]. The preprocessing and filtration stages were crucial in addressing the contamination of EEG recordings by a variety of artifacts, from biological to environmental origins.…”

Section: Resultsmentioning

confidence: 99%

EmotionNet: Dissecting Stress and Anxiety Through EEG-based Deep Learning Approaches

Daadaa

2024

IJACSA

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Amid global health crises, such as the COVID-19 pandemic, the heightened prevalence of mental health disorders like stress and anxiety has underscored the importance of understanding and predicting human emotions. Introducing "EmotionNet," an advanced system that leverages deep learning and state-of-the-art hardware capabilities to predict emotions, specifically stress and anxiety. Through the analysis of electroencephalography (EEG) signals, EmotionNet is uniquely poised to decode human emotions in real time. To get information from pre-processed EEG signals, the EmotionNet architecture combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks in a way that works well together. This dual approach first decomposes EEG signals into their core alpha, beta, and theta rhythms. We preprocess these decomposed signals and develop a CNN-LSTM-based architecture for feature extraction. The LSTM captures the intricate temporal dynamics of EEG signals, further enhancing understanding. The end process discerningly classifies signals into "stress" or "anxiety" states through the AdaBoost classifier. Evaluation against the esteemed DEEP, SEED, and DASPS datasets showcased EmotionNet's exceptional prowess, achieving a remarkable accuracy of 98.6%, which surpasses even human detection rates. Beyond its technical accomplishments, EmotionNet emphasizes the paramount importance of addressing and safeguarding mental health.

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“…Indeed, recognizing emotional states has increasingly garnered interest among researchers in neuroscience [3,4] and mental disorders [5][6][7][8]. The electroencephalography (EEG) is a suitable technique used for emotions recognition [9][10][11][12]. This is because EEG has portability advantages, lower cost, better tolerability, and higher signal temporal resolution than functional magnetic resonance imaging.…”

Section: Introductionmentioning

confidence: 99%

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

Bagherzadeh,

Shalbaf,

Shoeibi

et al. 2024

IEEE Access

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The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out crossvalidation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pretrained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

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EEG-based emotion recognition using random Convolutional Neural Networks

Cited by 22 publications

References 43 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

EmotionNet: Dissecting Stress and Anxiety Through EEG-based Deep Learning Approaches

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

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