Epilepsy makes the patients suffer great pain and has a very bad impact on daily life. In this paper, a novel method is proposed to implement electroencephalogram (EEG)-based epilepsy detection, in which multi-frequency multilayer brain network and deep learning are jointly utilized. Firstly, based on the multi-frequency characteristics of brain, we construct a multilayer brain network from the multi-channel EEG signals. The time, frequency and channel-related information from EEG signals are all mapped into the multilayer network topology, making it an effective feature for epilepsy detection. Subsequently, with multilayer brain network as input, a multilayer deep convolutional neural network (MDCNN) model is designed. MDCNN model has two blocks and uses a parallel multi-branch design in the first block, which exactly matches the multilayer structure of the proposed brain network. The experimental results on publicly available CHB-MIT datasets show that the proposed method can accurately detect epilepsy, with a high average accuracy of 99.56%, sensitivity of 99.29%, and specificity of 99.84%. All these provide an efficient solution for characterizing the complex brain states using multi-channel EEG signals.

Visual evoked potential (VEP)-related EEG signals have attracted widespread attention in the construction of brain-computer interface (BCI) systems. However, long-term use of the BCI system is prone to fatigue. Effectively coping with fatigue is the key to expanding BCI application scenarios. In this brief, we conduct two VEP experiments and propose a novel, concise and practical convolutional neural network (CNN) model to study and solve this problem. In detail, firstly, in terms of the stimulus frequencies in VEP experiments, we intercept the spectrum sequences of the fundamental waves and two harmonics. All these spectrum sequences are then paralleled input into the proposed multi-harmonic linkage CNN (MHLCNN) model, through three branches. Finally, the linkage characteristics of multiple harmonics are combined for VEP signal classification. The experimental results demonstrate that the proposed model shows good scalability, and can respectively improve the average classification accuracy by 23.17% and 15.85% when applied to steady-state VEP (SSVEP) signals in fatigue state and steady-state motion VEP (SSMVEP) signals, compared with the benchmark methods. All these provide a feasible and high-quality solution for reducing the impact of fatigue on the performance of VEP-based BCI systems.

Brain-computer interface (BCI) establishes an interactive platform by translating brain activity patterns into commands of external devices. BCIs, especially motor imagery (MI)-based BCIs, have injected new vitality into the development of rehabilitation medicine and many other fields. In this work, one convolutional neural network, named as Flashlight-Net model, is proposed for multiclass MI classification. Flashlight-Net model adopts modular design, in which channel fusion module and time domain module ensure the directions of feature extraction, while feature pool module reduces the loss of effective information. Given the multi-frequency nature of the brain, we combine three frequency bands and construct an ensemble Flashlight-Net model. During the model training, by means of transfer learning, pretraining and fine-tuning processes are designed to integrate training samples from multiple subjects. The experimental results on publicly available BCI Competition IV-2a dataset show that the proposed model can achieve good results on all nine subjects, with an average classification accuracy of 81.23% for four classes. All these demonstrate that the proposed Flashlight-Net model can effectively decode multi-channel and multiclass MI signals.

Characterizing the nonlinear dynamic behaviors of gas-liquid two-phase flow is a challenging but rewarding research topic. In this paper, a novel multi-layer visibility graph-based ordinal network (MVGON) is proposed for exploring the gas-liquid flow behaviors. In particular, the vertical upward gas-liquid flow experiments are first conducted in a 50 mm inner-diameter pipe and multivariate measurement data is acquired. Then, MVGON is inferred from experimental signals. MVGON not only can effectively fuse multivariate time series but also enables to further excavate concise key information based on inheriting system features extracted by visibility graph. For each derived projection network of MVGON, graph energy and average clustering coefficient are both calculated for quantitatively characterizing the topological structure of MVGON. In addition, permutation entropy and multivariate pseudo Wigner distribution-based time-frequency distribution are calculated for the three typical flow patterns to support our findings. The results indicate that our MVGON framework allows effectively characterizing the nonlinear dynamic behaviors during the evolution of different gas-liquid flow patterns. Meanwhile, it provides a novel approach for characterizing the complex system dynamics based on multivariate time series analysis.