Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Exploring EEG Features in Cross-Subject Emotion Recognition

Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the "leave-one-subject-out" verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.

Efficiently recognizing emotions is a critical pursuit in brain–computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition. These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups. The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83–92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects. Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition

A Systematic Review of The Potential Use of Neurofeedback in Patients with Schizophrenia.

Schizophrenia (SCZ) is a neurodevelopmental disorder characterized by positive symptoms (hallucinations and delusions), negative symptoms (anhedonia, social withdrawal) and marked cognitive deficits (memory, executive function, and attention). Current mainstays of treatment, including medications and psychotherapy, do not adequately address cognitive symptoms, which are essential for everyday functioning. However, recent advances in computational neurobiology have rekindled interest in neurofeedback (NF), a form of self-regulation or neuromodulation, in potentially alleviating cognitive symptoms in patients with SCZ. Therefore, we conducted a systematic review of the literature for NF studies in SCZ to identify lessons learned and to identify steps to move the field forward. Our findings reveal that NF studies to date consist mostly of case studies and small sample, single-group studies. Despite few randomized clinical trials, the results suggest that NF is feasible and that it leads to measurable changes in brain function. These findings indicate early proof-of-concept data that needs to be followed up by larger, randomized clinical trials, testing the efficacy of NF compared to well thought out placebos. We hope that such an undertaking by the field will lead to innovative solutions that address refractory symptoms and improve everyday functioning in patients with SCZ