Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.

Introduction: As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities. Methods: This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution. Results: Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively. Discussion: In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.

Compressive Video Recovery Using Block Match Multi-Frame Motion Estimation Based on Single Pixel Cameras.

Compressive sensing (CS) theory has opened up new paths for the development of signal processing applications. Based on this theory, a novel single pixel camera architecture has been introduced to overcome the current limitations and challenges of traditional focal plane arrays. However, video quality based on this method is limited by existing acquisition and recovery methods, and the method also suffers from being time-consuming. In this paper, a multi-frame motion estimation algorithm is proposed in CS video to enhance the video quality. The proposed algorithm uses multiple frames to implement motion estimation. Experimental results show that using multi-frame motion estimation can improve the quality of recovered videos. To further reduce the motion estimation time, a block match algorithm is used to process motion estimation. Experiments demonstrate that using the block match algorithm can reduce motion estimation time by 30%.

Signal reconstruction of the slow wave and spike potential from electrogastrogram.

The gastric slow wave and the spike potential can correspondingly represent the rhythm and the intensity of stomach motility. Because of the filtering effect of biological tissue, electrogastrogram (EGG) cannot measure the spike potential on the abdominal surface in the time domain. Thus, currently the parameters of EGG adopted by clinical applications are only the characteristics of the slow wave, such as the dominant frequency, the dominant power and the instability coefficients. The limitation of excluding the spike potential analyses hinders EGG from being a diagnosis to comprehensively reveal the motility status of the stomach. To overcome this defect, this paper a) presents an EGG reconstruction method utilizing the specified signal components decomposed by the discrete wavelet packet transform, and b) obtains a frequency band for the human gastric spike potential through fasting and postprandial cutaneous EGG experiments for twenty-five human volunteers. The results indicate the lower bound of the human gastric spike potential frequency is 0.96±0.20 Hz (58±12 cpm), and the upper bound is 1.17±0.23 Hz (70±14 cpm), both of which have not been reported before to the best of our knowledge. As an auxiliary validation of the proposed method, synchronous serosa-surface EGG acquisitions are carried out for two dogs. The frequency band results for the gastric spike potential of the two dogs are respectively 0.83-0.90 Hz (50-54 cpm) and 1.05-1.32 Hz (63-79 cpm). They lie in the reference range 50-80 cpm proposed in previous literature, showing the feasibility of the reconstruction method in this paper.

A model of two-way selection system for human behavior.

Two-way selection is a common phenomenon in nature and society. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytical solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller group's people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two-way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.

[The application of wavelet transform and wavelet lifting in signal processing of EGG].

This paper introduced the definition, structure of basis wavelet functions and wavelet lifting. Wavelet transform and wavelet lifting were applied in signal processing of EGG in our study. The characteristics of signal energy, information entropy and joint entropy were analyzed to introduce general selective method of wavelet basis functions after the signals have been filtered. In order to verify the rationality of evaluation criteria, signals of electrogastrogram (EGG) were processed and filtered with different wavelet basis functions in the experiments. Signals of EGG were filtered by wavelet transform and wavelet lifting, slow wave and spike wave of EGG were filtered. And the rationality of wavelet transform, wavelet lifting and effectiveness of EGG filter algorithm were proved by the experimental data. It provided an effective solution for the diagnosis and measurement of gastric diseases.

[Study on the removal method of electrogastrogram baseline wander based on wavelet transformation].

This paper presents a removal method of electrogastrogram (EGG) baseline wander based on wavelet transformation. The basic idea of this method is using the low-frequency signal which is obtained through multi-scale decomposition of EGG signals to approximate the baseline wander of EGG, so the component of baseline wander is filtered out from the sampling EGG signals. The method was applied successfully to process the experimental data of dog EGG in our laboratory. The experimental data and analysis of results showed that this method could filter out the baseline wander of EGG, and this method would not affect the gastric spike and slow wave bandwidth signals, which could be shown from the characteristics of bandwidth filter of wavelet transformation.

A real-time weighted-eigenvector MUSIC method for time-frequency analysis of electrogastrogram slow wave.

The surface electrogastrogram (EGG) records the electrical slow wave of the stomach noninvasively, whose frequency is a useful clinical indicator of the state of gastric motility. Estimators based on the periodogram method are widely adopted to obtain this parameter. But they are with a poor frequency domain resolution when the data window is short in time-frequency analysis, and have not taken full advantage of the slow wave model. We present a modified multiple signal classification (MUSIC) method for computing the frequency from surface EGG records, developing it into a real-time time-frequency analysis algorithm. Simulations indicate that the modified MUSIC method has better performance in resolution and precision in the sinusoid-like resultant signal frequency detecting than periodogram. Volunteer data tests show that the modified MUSIC method is stable and efficient for clinical applications, and reduces the danger of pseudo peaks for the diagnosis.