EEG-Based Emotion Classification Using Stacking Ensemble Approach

Chatterjee, Subhajit; Byun, Yung-Cheol

doi:10.3390/s22218550

Cited by 31 publications

(11 citation statements)

References 40 publications

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“…Accuracy (%) fNIRS 86.70 [55] 89.49 [56] 91.44 [57] EEG 98.78 [58] 99.55 [59] 99.57 [60] fNIRS + EEG 99.81…”

Section: Modalitymentioning

confidence: 99%

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Chen,

Yu,

Wang

et al. 2024

Electronics

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Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system’s excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms.

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“…Accuracy (%) fNIRS 86.70 [55] 89.49 [56] 91.44 [57] EEG 98.78 [58] 99.55 [59] 99.57 [60] fNIRS + EEG 99.81…”

Section: Modalitymentioning

confidence: 99%

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Chen,

Yu,

Wang

et al. 2024

Electronics

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“…The proposed method considers only the Fp1-Fp2 channel pair from which the alpha band’s variance and PSD were computed, by that minimizing the computational overhead whilst achieving reliable performance making it suitable for wearable EEG headsets used in real-time applications [ 26 , 111 ]. Overall, the results attained here are quite promising.…”

Section: Discussionmentioning

confidence: 99%

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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“…This method unifies various machine-learning methods and shares similarities with other ensemble approaches, such as bagging and boosting [ 30 ]. Stacking’s architecture is divided into two stages: level 0 and level 1 [ 31 ]. In level 0, base classifiers are trained using the whole training set, and each base classifier conducts classification on the data and generates its predictions.…”

Section: Review Of Literaturementioning

confidence: 99%

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

Idris,

Ismail,

Jaya

et al. 2024

PLoS ONE

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Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

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EEG-Based Emotion Classification Using Stacking Ensemble Approach

Cited by 31 publications

References 40 publications

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System

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

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus

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