Xin Zhang

Zhengqing Miao, Xin Zhang, Meirong Zhao et al.

EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. Hence, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller paradigms, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for event-related potentials (ERPs) and event-related desynchronization/synchronization (ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general online decoding model for various EEG tasks.

Zhengqing Miao, Xin Zhang, Carlo Menon et al.

Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG is non-stationary with low signal-to-noise, classifying motor imagery tasks of the same participant from different EEG recording sessions is generally challenging, as EEG data distribution may vary tremendously among different acquisition sessions. Although it is intuitive to consider the cross-session MI classification as a domain adaptation problem, the rationale and feasible approach is not elucidated. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The proposed framework can be easily applied to most existing artificial neural networks without altering the network structure, which facilitates our method with great flexibility and transferability. In the proposed framework, domain invariants were firstly constructed jointly with channel normalization and Euclidean alignment. Then, embedding features from source and target domain were mapped into the Reproducing Kernel Hilbert Space (RKHS) and aligned accordingly. A cosine-based center loss was also integrated into the framework to improve the generalizability of the SDDA. The proposed framework was validated with two classic and popular convolutional neural networks from BCI research field (EEGNet and ConvNet) in two MI-EEG public datasets (BCI Competition IV IIA, IIB). Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15.2%, 10.2% respectively in IIA dataset, and 5.5%, 4.2% in IIB dataset. The final MI classification accuracy reached 82.01% in IIA dataset and 87.52% in IIB, which outperformed the state-of-the-art methods in the literature.

Xin Zhang, Weixuan Kou, Eric I-Chao Chang et al.

This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-level features. Low-level features capture temporal and frequency domain properties and mid-level features learn compositions and structural information of signals. Since sleep staging is a sequential problem with long-term dependencies, we take advantage of RNNs with Bidirectional Long Short-Term Memory (BLSTM) architectures for sequence data learning. To simulate the actual situation of daily sleep, experiments are conducted with a resting group in which sleep is recorded in resting state, and a comprehensive group in which both resting sleep and non-resting sleep are included.We evaluate the algorithm based on an eight-fold cross validation to classify five sleep stages (W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision, recall and F1 score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%, 61.1%, and 58.5% in the comprehensive group, respectively. Various comparison experiments demonstrate the effectiveness of feature learning and BLSTM. We further explore the influence of depth and width of RNNs on performance. Our method is specially proposed for wearable devices and is expected to be applicable for long-term sleep monitoring at home. Without using too much prior domain knowledge, our method has the potential to generalize sleep disorder detection.