Emotion Recognition Based on Framework of BADEBA‐SVM

Wang, Zhongmin; Zhang, Zhaoping; Wen-lang, Wang

doi:10.1155/2019/9875250

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Secondly, the dimensionality of our feature vector remains extensive for real-time applications. Integrating our method with feature selection techniques such as Particle Swarm Optimization (PSO) [ 60 ], BAT algorithm [ 61 ], genetic algorithm (GA) [ 42 ], Whale Optimization Algorithm (WOA) [ 62 ], and other heuristic optimization methods might mitigate the “curse of dimensionality” and enhance classification performance. Thirdly, although we achieved high accuracy using the selected channels and SVM with default settings, optimizing SVM parameters remains unaddressed.…”

Section: Discussionmentioning

confidence: 99%

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Hag,

Al-Shargie,

Handayani

et al. 2023

Brain Sciences

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Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method’s uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time–frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

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Section: Discussionmentioning

confidence: 99%

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Hag,

Al-Shargie,

Handayani

et al. 2023

Brain Sciences

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show abstract

“…A combination of DE and the Bat Algorithm [181], was proposed in [182]. The proposed Binary Adaptive Differential Evolution Bat Algorithm (BADEBA) was tested on the DEAP using an SVM classifier.…”

Section: Swarm Intelligence For Channel Sets Discoverymentioning

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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“…Table II-B shows the state-of-the-art methods for EEG features and channels selection. Studies [45], [46], [48]- [53] attempted to estimate the best features using features selection (FS) methods including sequential feedforward selection (SFFS), Minimum Redundancy Maximum Relevance (mRMR), genetic algorithm (GA), evolutionary computation (EC) and sparse discriminative ensemble learning (SDEL), Sparse Discriminative Ensemble Learning (SDEL) algorithm, sparse linear discriminant analysis (LDA), (SBS), and principle component analysis (PCA) whereas study [54] used binary adaptive differential evolution bat algorithm (BADEBA) channels selection (ChS) method.…”

Section: B Eeg Features and Channels Selectionmentioning

confidence: 99%

Multichannel Optimization With Hybrid Spectral- Entropy Markers for Gender Identification Enhancement of Emotional-Based EEGs

et al. 2021

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Investigating gender differences based on emotional changes supports automatic interpretation of human intentions and preferences. This allows emotion applications to respond better to requirements and customize interactions based on affective responses. The electroencephalogram (EEG) is a tool that potentially can be used to detect gender differences. The main purpose of this paper is twofold. Firstly, it aims to use both linear and nonlinear features of EEG signals to identify emotional influences on gender behavior. Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. The EEGs of thirty healthy students from the University of Vienna have watched four short video clips depicting the emotions of anger, happiness, sadness and neutral. In this study, the wavelet transform (WT) de-noising technique, linear spectral mean frequency (meanF ) and nonlinear multiscale fuzzy entropy (M F E) features were used. The individual performance of these attributes was statistically examined using analysis of variance (ANOVA) to represent the gender behavior in the brain-emotion in females and males. Then, these two features were fused into a set of hybrid spectral-entropy attributes (SEA). Consequently, optimization algorithms including binary gravitation search algorithm (BGSA) and binary particle swarm optimization (BPSO), were employed to identify the optimal channels for gender classification. Finally, the k-nearest neighbors (kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. The results show linear and nonlinear features are remarkable neuromarkers for investigating gender-based differences in emotional states. Moreover, the results show significant enhancement in the overall accuracy of classification achieved by using the BGSA optimization algorithm with the proposed hybrid SEA set when compared to individual features. Therefore, the proposed methods were effective in improving the process of automatic gender recognition from the emotional-based EEG signals.

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Emotion Recognition Based on Framework of BADEBA‐SVM

Cited by 8 publications

References 25 publications

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

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

Multichannel Optimization With Hybrid Spectral- Entropy Markers for Gender Identification Enhancement of Emotional-Based EEGs

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