Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest

Emotion is the psychological and physiological response of human to external things, and occupies an important place in the study of human-computer interaction. Electroencephalogram (EEG) is a physiological signal that is widely used in the field of emotion awareness recognition. EEG signals can be divided into 5 basic rhythms according to frequency, and most of the existing research on emotion awareness recognition based on EEG signals directly processes the rhythms and extracts the corresponding features. This method is easy to lose the rhythm information and cannot give full play to its function. Therefore, in this study, we propose an optimized deep forest emotion awareness recognition method based on EEG rhythm characteristics to investigate the effect of rhythms on emotion awareness recognition performance. Firstly, we classify EEG rhythms into 3 types of rhythm combinations: single rhythm, compound rhythm and full rhythm according to the principle of adjacent combination, and fully consider various combination forms of rhythms; Secondly, we construct a two-dimensional input model to retain the spatial information of multi-channel EEG signals; Finally, we use the gcForest classification model for emotion recognition, which does not require feature extraction and maximizes the retention of rhythm information. We conducted extensive experiments on the DEAP dataset, and the experimental results show that the frequency band and number of rhythms affect the performance of emotion recognition, and the high frequency band rhythms have better emotion classification performance compared with the low frequency band rhythms, among which the $\beta$ rhythms are higher than the Y rhythms in validity and arousal dimension, and their classification accuracy is 96.711% and 96.633%; the classification accuracy of compound rhythms was higher than that of single rhythms, but a higher number of compound rhythms does not necessarily lead to better emotion classification performance, where in the arousal dimension, compound rhythms $((\mathrm{x}+\beta+\gamma)$ have better awareness emotion recognition performance compared to full rhythms $((+\mathrm{t}\mathrm{K}+\beta+\gamma)$, with 97% classification accuracy.

Optimized Deep Forest Emotional Awareness Recognition Based on EEG Rhythm Characteristics

Mental workload level can reflect subjects’ personal ability. In addition, continuous high level of mental workload can reduce subjects’ performance level, so it is necessary to detect subjects’ mental workload level in the time. In this paper, we propose a CNN model for time-frequency analysis based on Siamese networks (Siamese-EEGNet), in which the original Electroencephalogram (EEG) signal and the Power Spectral Density (PSD) of the signal are used as model inputs, and the features of the signal are extracted layer by layer through convolutional layers. Using P3 as a measure, the model is pretrained on a large volume data set using transfer learning, and successfully transfer to a smaller volume data set with mental workload level by fine-tuning the model parameters. SiameseEEGNet is able to consider both time domain and frequency domain information in the data, which is suitable for EEG structure characteristics. In practical completion, it can detect the mental workload level of subjects, measure their individual ability and improve their performance level.

A Siamese Network Fusion Time-Frequency Domain Features for Mental Workload Level Identification

Face alignment is an important research field in machine vision, which have applied in face recognition, expression recognition, pose estimation, and face synthesis. However, many algorithms are design for faces in small poses and good lighting, they do not perform well in the wild. In this paper, we propose a face alignment algorithm to improve the accuracy of face landmark. This algorithm begins with a network to locate face landmark in general and employs a face normalization to reduce disturbing redundant information. Then a 3D facial reconstruction is introduced to get exact camera matrix. Euler angle based on 3D reconstruction eliminates the effect of pose for face landmark. Experiments on the IBUG dataset show that the algorithm achieves improvement over state-of-the-art algorithms.

Face Landmark Calibration Based on 3D Reconstruction and Deep Learning

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Papers

Optimized Deep Forest Emotional Awareness Recognition Based on EEG Rhythm Characteristics

A Siamese Network Fusion Time-Frequency Domain Features for Mental Workload Level Identification

Face Landmark Calibration Based on 3D Reconstruction and Deep Learning