Electroencephalography (EEG) emotion recognition, an important task in Human-Computer Interaction (HCI), has made a great breakthrough with the help of deep learning algorithms. Although the application of attention mechanism on conventional models has improved its performance, most previous research rarely focused on multiplex EEG features jointly, lacking a compact model with unified attention modules. This study proposes Joint-Dimension-Aware Transformer (JDAT), a robust model based on squeezed Multi-head Self-Attention (MSA) mechanism for EEG emotion recognition. The adaptive squeezed MSA applied on multidimensional features enables JDAT to focus on diverse EEG information, including space, frequency, and time. Under the joint attention, JDAT is sensitive to the complicated brain activities, such as signal activation, phase-intensity couplings, and resonance. Moreover, its gradually compressed structure contains no recurrent or parallel modules, greatly reducing the memory and complexity, and accelerating the inference process. The proposed JDAT is evaluated on DEAP, DREAMER, and SEED datasets, and experimental results show that it outperforms state-of-the-art methods along with stronger flexibility.

The morphology of Fetal Electrocardiogram (FECG) plays an important role in the early diagnosis of fetal health condition. However, it is intractable to extract the clean morphology of FECG signals, which are usually contaminated by Maternal ECG (MECG) and various noises. To extract the clean morphology of FECG signals from noninvasive abdominal ECG records, a high-performance and high-efficient two-stage Slow-Fast Long Short Term Memory (SFLSTM) based architecture is proposed. The MECG elimination and the FECG enhancement are realized by the elaborately designed slow LSTM and fast LSTM to filter out the MECG and the residual noise components, respectively. Qualitative and quantitative experiments are conducted on the records from two public databases. The experimental results show that our proposed MECG elimination and FECG enhancement schemes improve the Signal-to-Noise Ratio (SNR) by 3.09 dB and 1.81 dB, respectively. The proposed fast LSTM reduces the amount of computation by approximately 50%, without any degradation in performance. Our proposed method may leverage the noninvasive FECG monitoring for the early detection of fetal heart diseases.