Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.

Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.

TASA: Temporal Attention With Spatial Autoencoder Network for Odor-Induced Emotion Classification Using EEG.

The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness of Electroencephalogram (EEG) make it suitable to objectively study human preferences for odors. Effectively learning the temporal dynamics and spatial information from EEG is crucial for detecting odor-induced emotional valence. In this paper, we propose a deep learning architecture called Temporal Attention with Spatial Autoencoder Network (TASA) for predicting odor-induced emotions using EEG. TASA consists of a filter-bank layer, a spatial encoder, a time segmentation layer, a Long Short-Term Memory (LSTM) module, a multi-head self-attention (MSA) layer, and a fully connected layer. We improve upon the previous work by utilizing a two-phase learning framework, using the autoencoder module to learn the spatial information among electrodes by reconstructing the given input with a latent representation in the spatial dimension, which aims to minimize information loss compared to spatial filtering with CNN. The second improvement is inspired by the continuous nature of the olfactory process; we propose to use LSTM-MSA in TASA to capture its temporal dynamics by learning the intercorrelation among the time segments of the EEG. TASA is evaluated on an existing olfactory EEG dataset and compared with several existing deep learning architectures to demonstrate its effectiveness in predicting olfactory-triggered emotional responses. Interpretability analyses with DeepLIFT also suggest that TASA learns spatial-spectral features that are relevant to olfactory-induced emotion recognition.

MTDN: Learning Multiple Temporal Dynamics Representation for Emotional Valence Classification with EEG.

Emotion recognition from electroencephalogram (EEG) requires computational models to capture the crucial features of the emotional response to external stimulation. Spatial, spectral, and temporal information are relevant features for emotion recognition. However, learning temporal dynamics is a challenging task, and there is a lack of efficient approaches to capture such information. In this work, we present a deep learning framework called MTDN that is designed to capture spectral features with a filterbank module and to learn spatial features with a spatial convolution block. Multiple temporal dynamics are jointly learned with parallel long short-term memory (LSTM) embedding and self-attention modules. The LSTM module is used to embed the time segments, and then the self-attention is utilized to learn the temporal dynamics by intercorrelating every embedded time segment. Multiple temporal dynamics representations are then aggregated to form the final extracted features for classification. We experiment on a publicly available dataset, DEAP, to evaluate the performance of our proposed framework and compare MTDN with existing published results. The results demonstrate improvement over the current state-of-the-art methods on the valence dimension of the DEAP dataset.

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-global-graph network (LGGNet), a novel neurologically inspired graph neural network (GNN), to learn local-global-graph (LGG) representations of electroencephalography (EEG) for brain-computer interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multiscale 1-D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local-and global-graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art (SOTA) methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, regularized graph neural network (RGNN), attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN), hierarchical recurrent neural network (HRNN), and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant ( ) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG.

Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment.

Although disinfection is key to infection control, the colonization patterns and resistomes of hospital-environment microbes remain underexplored. We report the first extensive genomic characterization of microbiomes, pathogens and antibiotic resistance cassettes in a tertiary-care hospital, from repeated sampling (up to 1.5 years apart) of 179 sites associated with 45 beds. Deep shotgun metagenomics unveiled distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human-microbiome-influenced environments with corresponding patterns of spatiotemporal divergence. Quasi-metagenomics with nanopore sequencing provided thousands of high-contiguity genomes, phage and plasmid sequences (>60% novel), enabling characterization of resistome and mobilome diversity and dynamic architectures in hospital environments. Phylogenetics identified multidrug-resistant strains as being widely distributed and stably colonizing across sites. Comparisons with clinical isolates indicated that such microbes can persist in hospitals for extended periods (>8 years), to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in hospitals and establish the feasibility of systematic surveys to target resources for preventing infections.

Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes.

Characterization of microbiomes has been enabled by high-throughput metagenomic sequencing. However, existing methods are not designed to combine reads from short- and long-read technologies. We present a hybrid metagenomic assembler named OPERA-MS that integrates assembly-based metagenome clustering with repeat-aware, exact scaffolding to accurately assemble complex communities. Evaluation using defined in vitro and virtual gut microbiomes revealed that OPERA-MS assembles metagenomes with greater base pair accuracy than long-read (>5×; Canu), higher contiguity than short-read (~10× NGA50; MEGAHIT, IDBA-UD, metaSPAdes) and fewer assembly errors than non-metagenomic hybrid assemblers (2×; hybridSPAdes). OPERA-MS provides strain-resolved assembly in the presence of multiple genomes of the same species, high-quality reference genomes for rare species (<1%) with ~9× long-read coverage and near-complete genomes with higher coverage. We used OPERA-MS to assemble 28 gut metagenomes of antibiotic-treated patients, and showed that the inclusion of long nanopore reads produces more contiguous assemblies (200× improvement over short-read assemblies), including more than 80 closed plasmid or phage sequences and a new 263 kbp jumbo phage. High-quality hybrid assemblies enable an exquisitely detailed view of the gut resistome in human patients.