Emotion Classification Using EEG Signals

Dabas, Harsh; Sethi, Chaitanya; Dua, Chirag; Dalawat, Mohit; Sethia, Divyashikha

doi:10.1145/3297156.3297177

Cited by 45 publications

(31 citation statements)

References 8 publications

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“…As described earlier, such result is only slightly better than random chance, and since the designed system took in all 40 of the channels of the epoch data as the features, the discrepancy and errors were more, as other channels did not change much throughout the 60 s videos. According to [34,35], it is suggested that by using PCA they were able to find the channels that gave the best possible results, which also is aligned with the channels "F3, C3, F4, C4, AF3, PO4, CP1" as crucial in obtaining the best possible results. These channels indicated that the bottom left hemisphere of the brain was responsible for triggering emotional states.…”

Section: Resultsmentioning

confidence: 99%

A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals

2020

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IntroductionEmotion classification requires a study of various factors and variables including common sense emotions of happiness or anger with varying degrees. These qualities and intensities are second nature to humankind and their detection by facial recognition AbstractEmotion recognition using brain signals has the potential to change the way we identify and treat some health conditions. Difficulties and limitations may arise in general emotion recognition software due to the restricted number of facial expression triggers, dissembling of emotions, or among people with alexithymia. Such triggers are identified by studying the continuous brainwaves generated by human brain. Electroencephalogram (EEG) signals from the brain give us a more diverse insight on emotional states that one may not be able to express. Brainwave EEG signals can reflect the changes in electrical potential resulting from communications networks between neurons. This research involves analyzing the epoch data from EEG sensor channels and performing comparative analysis of multiple machine learning techniques [namely Support Vector Machine (SVM), K-nearest neighbor, Linear Discriminant Analysis, Logistic Regression and Decision Trees each of these models] were tested with and without principal component analysis (PCA) for dimensionality reduction. Grid search was also utilized for hyper-parameter tuning for each of the tested machine learning models over Spark cluster for lowered execution time. The DEAP Dataset was used in this study, which is a multimodal dataset for the analysis of human affective states. The predictions were based on the labels given by the participants for each of the 40 1-min long excerpts of music. music. Participants rated each video in terms of the level of arousal, valence, like/dislike, dominance and familiarity. The binary class classifiers were trained on the time segmented, 15 s intervals of epoch data, individually for each of the 4 classes. PCA with SVM performed the best and produced an F1-score of 84.73% with 98.01% recall in the 30th to 45th interval of segmentation. For each of the time segments and "a binary training class" a different classification model converges to a better accuracy and recall than others. The results prove that different classification models must be used to identify different emotional states.

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Section: Resultsmentioning

confidence: 99%

A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals

2020

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“…EEG signals also have a relatively low signal-to-noise ratio (SNR) and are susceptible to distortion from artificial interference (e.g., eye movement) [28]. To remove these artifacts and make EEG signals more correlated to the target events, EEG signals are usually calculated in five frequency bands, i.e., delta (1-3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta (14-30 Hz), and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) [27]. Useful features can be extracted from these five frequency bands of EEG signals for detailed analysis on specific tasks [29,30].…”

Section: Characteristics Of Eeg Signalsmentioning

confidence: 99%

“…To compare the effectiveness of the examined features from different TW lengths, six classical classifiers were used for emotion recognition, including KNN, LR, SVM, GNB, Multilayer Perceptron (MLP), and Bootstrap Aggregating (Bagging). These classifiers are the most frequently ones used with high accuracies and strong adaptabilities to different classification tasks [15,42,43]. A machine learning module in Python called sklearn was used to construct models, and the relevant parameter settings are listed in Table 3.…”

Section: Extracting Features Based On Twmentioning

confidence: 99%

The Effect of Time Window Length on EEG-Based Emotion Recognition

Ouyang

Yuan

et al. 2022

Sensors

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Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

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“…Neural Network [31][32][33] 85.80% Support Vector Machine [34][35][36] 77.80% K-Nearest Neighbor [33,37,38] 88.94% Multi-layer Perceptron [38][39][40] 78.16% Bayes [41][42][43] 69.62% Extreme Learning Machine [41] 87.10% K-Means [43] 78.06% Linear Discriminant Analysis [42] 71.30% Gaussian Process [44] 71.30% To improve on the performance of the SOA methods, we used the features generated by M3GP in Figure 15. This kind of transfer learning was used in [45] with success and the best training transformation found in M3GP was used to transform the dataset into a new one (M3GP tree in Section 4.2), considering that these new features contain more information to simplify the learning process of the SOA methods.…”

Section: Classifier Average Performancementioning

confidence: 99%

EEG-Based Emotion Recognition Using Deep Learning and M3GP

et al. 2022

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This paper presents the proposal of a method to recognize emotional states through EEG analysis. The novelty of this work lies in its feature improvement strategy, based on multiclass genetic programming with multidimensional populations (M3GP), which builds features by implementing an evolutionary technique that selects, combines, deletes, and constructs the most suitable features to ease the classification process of the learning method. In this way, the problem data can be mapped into a more favorable search space that best defines each class. After implementing the M3GP, the results showed an increment of 14.76% in the recognition rate without changing any settings in the learning method. The tests were performed on a biometric EEG dataset (BED), designed to evoke emotions and record the cerebral cortex’s electrical response; this dataset implements a low cost device to collect the EEG signals, allowing greater viability for the application of the results. The proposed methodology achieves a mean classification rate of 92.1%, and simplifies the feature management process by increasing the separability of the spectral features.

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Emotion Classification Using EEG Signals

Cited by 45 publications

References 8 publications

A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals

A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals

The Effect of Time Window Length on EEG-Based Emotion Recognition

EEG-Based Emotion Recognition Using Deep Learning and M3GP

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