Electroencephalogram (EEG)-based emotion recognition is an important research area in affective computing and mental health assessment. To address the insufficient modeling of long-term dependencies in EEG signals, this paper proposes an EEG emotion recognition method based on multi-branch convolutional neural networks (CNN) and Transformer (MCT). The proposed method employs a multi-scale CNN to extract local temporal features from EEG signals and constructs a parallel CNN-Transformer architecture to capture both short-term variations and long-term dependencies, thereby enabling temporal feature modeling at different time scales. Furthermore, a dual-branch convolutional structure is utilized to learn both global and local spatial channel features of EEG signals. A convolutional block attention module (CBAM) is then introduced to fuse the spatio-temporal features of EEG signals. Experimental results show that the proposed MCT model achieves a classification accuracy of 83.83% on the Shanghai Jiao Tong University emotion EEG dataset (SEED). On the music emotion EEG dataset (MEEG), it attains accuracies of 90.00% and 92.62% for the arousal and valence dimensions, respectively. On the database for emotion analysis using physiological signals (DEAP), it attains accuracies of 61.30% and 61.04% for the arousal and valence dimensions. The accuracy results on all three datasets outperform those of the best-performing baseline models. These findings indicate that MCT can effectively learn discriminative features associated with emotional states, providing a new perspective for EEG-based emotion recognition research.
ObjectiveTo explore the methods of breast reconstruction surgery with laparoscopically harvested pedicled omental flap (LHPOF), and analyze the patient’ evaluation, operation process and postoperative follow-up. MethodsPatients with pathologically proven breast cancer or plasma cell mastitis who underwent LHPOF breast reconstructive surgery were retrospectively collected from the Department of Breast and Thyroid Surgery of The Second Affiliated Hospital of Chongqing Medical University from February 2022 to December 2023. ResultsA total of 16 patients were collected. The mean age of patients was 43.3 (ranging from 27 to 68) years old, the mean body mass index of patients was 23.0 kg/m2 (ranging from 18.3 to 28.6 kg/m2). One patient underwent transplant omental flat removal surgery due to postoperative flap thrombosis, and one patient choose to give up breast reconstruction due to insufficient flap volume. The single-stage surgery was performed successfully in the rest patients with no requirement of laparotomy. All patients made an uneventful recovery after surgery. During the follow-up period, which averaged 13 months and ranged from 9 to 17 months, the major symptoms were mild epigastric bulge (2 patients) and flap atrophy (1 patient), no serious flap-related or donor site-related complications such as flap loss, bowel dysfunction and abdominal incisional hernia. In general, the aesthetic results were satisfactory. ConclusionsUsing LHPOF in immediate breast reconstruction surgery can achieve satisfied aesthetic result, for the soft and natural appearance of the reconstructed breast. In the mean time, compared with other autologous tissue reconstruction approaches, LHPOF has lower incidences of complications of donor-site and flap-site.