Electroencephalography (EEG)-based emotion recognition is an important technology for human–computer interactions. In the field of neuromarketing, emotion recognition based on group EEG can be used to analyze the emotional states of multiple users. Previous emotion recognition experiments have been based on individual EEGs; therefore, it is difficult to use them for estimating the emotional states of multiple users. The purpose of this study is to find a data processing method that can improve the efficiency of emotion recognition. In this study, the DEAP dataset was used, which comprises EEG signals of 32 participants that were recorded as they watched 40 videos with different emotional themes. This study compared emotion recognition accuracy based on individual and group EEGs using the proposed convolutional neural network model. Based on this study, we can see that the differences of phase locking value (PLV) exist in different EEG frequency bands when subjects are in different emotional states. The results showed that an emotion recognition accuracy of up to 85% can be obtained for group EEG data by using the proposed model. It means that using group EEG data can effectively improve the efficiency of emotion recognition. Moreover, the significant emotion recognition accuracy for multiple users achieved in this study can contribute to research on handling group human emotional states.

During the advantages of shorter training and higher information throughput, steady-state visual evoked potential (SSVEP) is widely used in brain–computer interface (BCI) research. Recently, collecting EEG signals from the ear area (ear-EEG) has gained increasing attention because it is more comfortable and convenient than scalp electrodes. The ear-EEG-based BCI system based on ear electrodes has weaker signals and more noise components because the electrodes are located far away from the top of the head. In this study, the RandOm Convolutional KErnel Transform (ROCKET) algorithm integrated with the Morlet wavelet transform (Morlet-ROCKET) was proposed to solve this issue. This study compared the performence of Morlet-ROCKET with two established methods: canonical correlation analysis-based (FBCCA) and Transformer methods. The proposed Morlet-ROCKET model demonstrated superior performance across multiple measures, including increased classification accuracy in 1 s, 3 s, and 4 s time windows and higher area under the curve (AUC) values in receiver operating characteristic (ROC) analysis. The analysis result proved that with efficient data processing algorithms, ear-EEG-based BCI systems can also have good performance, and providing support for the popularization of BCI.

Electroencephalography (EEG)-based emotion recognition remains challenging under subject-independent evaluation because of substantial inter-subject variability. This study investigates the feasibility of state-fidelity-based quantum-kernel learning for subject-independent valence and arousal recognition using differential entropy (DE) and phase-locking value (PLV) features. Using the DEAP dataset, DE and PLV features were extracted and evaluated individually and in combination, with subject-wise normalization and F-score feature selection applied within the evaluation framework. Six features were retained for valence and eight for arousal. The selected features were encoded using a Qiskit ZFeatureMap, and the fidelity-based quantum kernel was evaluated through statevector simulation on a classical computer. Quantum-kernel, Linear, and RBF SVMs were compared using nested participant-grouped cross-validation across DE, PLV, and DE–PLV feature representations. For valence, the quantum-kernel SVM using DE features achieved the highest mean balanced accuracy of 59.18%. For arousal, the Linear SVM using DE features achieved the highest mean balanced accuracy of 54.16%. Overall, the three classifiers showed comparable performance across the evaluated feature representations. These results support the feasibility of state-fidelity-based quantum similarity for subject-independent EEG emotion recognition.

Electroencephalography (EEG)-based emotion recognition is an important technology for human-computer interactions. In the field of neuromarketing, emotion recognition based on group EEG can be used to analyze the emotional states of multiple users. Previous emotion recognition experiments have been based on individual EEGs; therefore, it is difficult to use them for estimating the emotional states of multiple users. The purpose of this study is to find a data processing method that can improve the efficiency of emotion recognition.In this study, the DEAP dataset was used, which comprises EEG signals of 32 participants that were recorded as they watched 40 videos with different emotional themes. This study compared emotion recognition accuracy based on individual and group EEGs using the proposed convolutional neural network model. Based on this study, we can see that the differences of phase locking value (PLV) exist in different EEG frequency bands when subjects are in different emotional states. The results showed that an emotion recognition accuracy of up to 85\% can be obtained for group EEG data by using the proposed model. It means that using group EEG data can effectively improve the efficiency of emotion recognition. Moreover, the significant emotion recognition accuracy for multiple users achieved in this study can contribute to research on handling group human emotional states.

NeuroFeedback (NF) is a method of altering the brain information of users, and it has been widely applied in the fields of medical and welfare. Recently, the research which aimed at improving cognitive function in healthy subjects has attracted a lot of attention. Many studies used EEG frequency bands and behavioral indices to improve and evaluate cognitive function. However, the evaluation of cognitive function using brain information indices mainly on the discussion of EEG frequency band. In addition, the frequency band has been mainly treated as a fixed band (alpha band et al.). In this study, we trained Upper Alpha (UA) based on individual alpha frequency using a proposed NF method, and not only confirm the changes in UA, but also examines the changes of P300. We found that training UA significantly reduced the latency of P300. In the future, we will use a control group to investigate whether the reduction of P300 latency is caused by NF and how the number of NF affects the latency of P300.