In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially electroencephalogram signals, has become a popular research topic and attracted wide attention. However, the imbalance of the data sets themselves, affective features’ extraction from electroencephalogram signals, and the design of classifiers with excellent performance, pose a great challenge to the subject. Motivated by the outstanding performance of deep learning approaches in pattern recognition tasks, we propose a method based on convolutional neural network with data augmentation method Borderline-synthetic minority oversampling technique. First, we obtain 32-channel electroencephalogram signals from DEAP data set, which is the standard data set of emotion recognition. Then, after data pre-processing, we extract features in frequency domain and data augmentation based on the data augmentation algorithm above for getting more balanced data. Finally, we train a one dimensional convolutional neural network for three classification on two emotional dimensions valence and arousal. Meanwhile, the proposed method is compared with some traditional machine learning methods and some existing methods by other researchers, which is proved to be effective in emotion recognition, and the average accuracy rate of 32 subjects on valence and arousal are 97.47% and 97.76% respectively. Compared with other existing methods, the performance of the proposed method with data augmentation algorithm Borderline-SMOTE shows its advantage in affective emotional recognition than that without Borderline-SMOTE.

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Effects of Data Augmentation Method Borderline-SMOTE on Emotion Recognition of EEG Signals Based on Convolutional Neural Network

Bimodal affect recognition based on autoregressive hidden Markov models from physiological signals.

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved.

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Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

Anxiety, nervousness and stress are daily challenges for mankind. These challenges are very severe specifically for students of the age group between years 14 to 25. Therefore it very important to develop the simplest, low cost, accurate and handy process which will be helpful for the society to gauge the anxiety levels and take necessary corrective actions to avoid health and psychological issues. It is of extreme importance to have regular checks on change in behavior and to ensure correct emotion analysis and take the corrective action. Article elaborates unique feature extraction method called as “linear formulation of differential entropy”. With this method we have significantly reduced number of (Electroencephalography) EEG channels for emotion detection. This work has discovered new approach in neuroscience. It’s proved that, single-channel EEG contains sufficient information for emotion recognition. The performance of the newly proposed technique is based on the “Database for Emotion Analysis using Physiological Signals” (DEAP) benchmark database using single channel FP1, prefrontal channel [FP1, AF3, FP2, AF4], and all 32 channel. Bidirectional long short term memory (BiLSTM) is used as classifier. The performance shows that, accuracy achieved using proposed method of single channel (FP1) is almost equivalent to the accuracy of 32 channels.

Optimal Number of Electrode Selection for EEG Based Emotion Recognition using Linear Formulation of Differential Entropy

In recent times, emotion recognition is in attention in brain computer interface (BCI) and human computer interaction (HCI) research area to provide a very good communication between brain and computer. The aim is to achieve a good recognition rate, although there are numerous researches have been conducted also there has been created several confusions with the definition of human emotions and the difference between emotions and moods. To detect brain signal, Electroencephalogram (EEG) signal has become biological marker. For its low cost, good time and spatial resolution EEG has been used widely in BCI researches. Extraction of features from EEG signals is one of the vital steps of EEG based emotion recognition. The appropriate feature selection for EEG based automatic emotion recognition is still now a big research topic. In this paper, a comprehensive survey is made on feature extraction methods and their comparative merits and limitations.

A Survey on Feature Extraction Methods for EEG Based Emotion Recognition

In our day-to-day life Emotions play an vital role. They help in identifying a human state of mind. Information about the human state of mind can significantly help in human-machine interaction and brain-computer interface. Some of the existing researchers have used speech, text, gesture or facial expressions for emotion recognition. However, these factors vary across culture and nation. Because of which, it is difficult to detect emotions more accurately. Hence, present work considers EEG signals for emotion recognition which not only ignores external factors but also helps to detect real emotions arising directly from our brain. A benchmark DEAP (Dataset for Emotion Analysis using Physiological signals) dataset is used for emotion investigation. A novel feature extraction technique called Frequency Cepstral Coefficient (FCC) is proposed to extract features from DEAP dataset. FCC technique is compared with Kernel Density Estimation (KDE) on DEAP dataset for feature extraction. These extracted features are then classified into two emotional states — happy and sad using K-Nearest Neighbor (K-NN) classifier. The selection of most appropriate and reliable method of feature extraction greatly helps for accurate classification. Number of experiments was conducted to evaluate the efficiency of feature extraction techniques. The experimental results show that KDE gives 80% accuracy and FCC outperforms it by achieving 90% accuracy on DEAP.

Prashant Lahane

Papers

A novel approach for analyzing human emotions based on electroencephalography (EEG)