Depression is a common emotional disorder in modern society that causes growing burdens globally. This mental disorder has been frequently confirmed to be closely related to abnormalities in the prefrontal cortex (PFC). However, it remains to be fully investigated whether the neural activity in the PFC has regular quasi-steady spatiotemporal structures, and whether dynamic patterns of these structures are associated with prefrontal dysfunction in depression. To further uncover such neural correlates, we extended the traditional electroencephalography (EEG) microstates to a novel localized level and developed a variational Bayesian probabilistic generative model to decode such localized microstate patterns from few-channel prefrontal EEG signals. We validated the method with a publicly available multichannel EEG dataset obtained from 165 healthy individuals and a three-channel prefrontal EEG dataset (43 depressed and 43 healthy). The approach was finally used to examine dynamic evolution of prefrontal neural activities in depression. Our results demonstrate that the localized prefrontal microstates exhibit high cross-dataset reproducibility and were characterized by finer spatiotemporal patterns independent from traditional whole-brain microstates. The results further revealed significant emotional task-specific abnormalities in localized prefrontal microstates between the depressed and the healthy individuals, including more frequent occurrences and shorter durations in high-power bilaterally asymmetric microstates, as well as less organized low-power symmetric microstates. Our extended concept of the localized microstates and associated analytical methods provides a novel theoretical framework for elucidating prefrontal neural dynamics and also lays a theoretical foundation for uncovering prefrontal functional abnormalities in depression and developing auxiliary diagnostic tools with prefrontal few-channel EEG data.

The advancement of wearable electronic technology has facilitated the integration of smart wearable devices into artificial intelligence (AI)-driven medical assisted diagnosis. Embedded multi-core processors (MPs) have gradually emerged as pivotal hardware components for smart wearable medical diagnostic devices due to their high performance and flexibility. However, embedded MPs face the challenge of balancing performance, power consumption, and load-balancing. In response, we introduce a Pareto-based iterated local search (PILS) algorithm for task scheduling, which systematically optimizes multiple objectives, alongside a task list model to reduce the dimension of the decision space and enhance scheduling performance. In addition, we present a two-stage discretization scheme to ensure that the proposed algorithm offers meaningful guidance throughout the scheduling process. Simulation and on-board testing results show that the proposed algorithm effectively optimizes energy consumption, task execution time, and load balancing in embedded MPs task scheduling, indicating the potential of the proposed algorithm in enhancing the performance of smart wearable medical diagnostic devices powered by embedded MPs.

Depression detection via wearable Electroencephalogram (EEG) sensor-assisted diagnosis system demands computationally efficient models compatible with resource-constrained edge devices. Spiking Neural Networks (SNNs) offer inherent advantages for processing the spatio-temporal patterns of EEG through event-driven manner. In this study, we innovatively present LSNNet, a lightweight SNN model specifically designed for wearable EEG sensors. The model exhibits low computational complexity with 7.18 K parameters and 67.68 M Floating-Point Operations (FLOPs). It requires only 246.88 KB of Random Access Memory (RAM) and 57.33 KB of Read-Only Memory (ROM) for on-board execution, and has been validated on both the single-core STM32U535CET6 and the multi-core GAP8 microcontrollers. Despite its minimal computational and memory requirements, LSNNet achieves impressive performance metrics, with a classification accuracy of 89.2%, specificity of 92.4%, and sensitivity of 86.4% in independent tests conducted on EEG data collected from 73 depressed patients and 108 healthy controls using our three-lead EEG sensor. Especially, when running on the GAP8 microcontrollers, the LSNNet model has a low power consumption of 21.43 mW and a satisfactory inference time of 0.63 s while maintaining a classification accuracy of 87.5% (only with a reduction of 1.98%). These results underscore the potential of integrating wearable EEG sensors with the LSNNet model for depression detection in the Internet of Things (IoT) era.

With social development, the demand for automatic sleep quality assessment in wearable devices is increasing, especially as sleep quality is closely related to the diagnosis of psychiatric disorders. However, existing automatic sleep stage classification models are mostly designed for unimodal signals, with an emphasis on increasing parameter scale and model depth. As a result, it is challenging to meet the requirements for both lightweight models and high accuracy in wearable device-based automatic sleep staging tasks. To address this problem, this study introduces a novel lightweight model for sleep staging, MultimodalSleepNet, which is based on multi-modal physiological signals. Specifically, the model is designed to capture the temporal dynamics of physiological signals and the spatial interactions between multimodal signals. Additionally, an inflationary convolution mechanism is incorporated to accelerate temporal feature extraction. We validate the model using the publicly available Sleep-EDF-Expanded dataset. Compared to similar studies, our model achieves outstanding performance, with accuracies of 93.1% and 90.2% in the three-stage and five-stage sleep recognition tasks, respectively. Notably, the three-stage classification results show an 11.9% improvement in modal fusion accuracy compared to unimodal signals, with an 8.9% improvement in multiclass F1 score and a 20.8% increase in Cohen’s kappa coefficient. In conclusion, our study offers a reference for the design of lightweight models for sleep staging and provides new insights into feature extraction and fusion of multimodal signals.

Previous studies have shown that the resting-state electroencephalogram (EEG) of depressed patients exhibits abnormal dynamic activation of large-scale networks in microstate analysis. However, the problem of heterogeneous results in microstate features as physiological markers for depressive disorders has not been addressed. An important factor contributing to this problem is that previous studies have overlooked the effects of eye-opening and eye-closing on resting-state EEG microstates in depression. To address this gap, the present study proposes a new microstate delineation method to accurately identify EEG microstates in both open-eye and closed-eye resting states, and further analyzes the differential performance of the depressed group and the control group in the two states. We validated our method on a dataset containing 64-channel EEG data (55 cases in the depressed group and 55 cases in the control group). The results showed that the classical seven microstate topographies were present in both open-eye and closed-eye conditions, which may explain why other studies have overlooked the impact of eye state on resting-state microstates in depression. In addition, the microstate characteristics of depression were differentially expressed in the open-eye and closed-eye conditions, with the depressed group showing more significant results in the closed-eye condition. Overall, our study demonstrates that eye state affects depression microstates and provides new insights to address the problem of heterogeneity in the results of EEG microstate studies of depression.

Depressive affective dysfunction could be manifested by dynamic reorganization processes of functional brain networks under emotional tasks. However, such emotional task-specific reorganizations remain not fully understood in terms of spatiotemporal organization of oscillation dynamics. The insufficiency of approaches for quantifying such dynamic reorganization limits the effective extraction of dynamic neuromarkers of depressive affective dysfunction. To address this gap, this study presents a neural oscillation-dependent microstate approach to quantify the dynamic reorganization process of functional networks at high temporal resolution. The approach was tested by analyzing a 64-channel electroencephalography (EEG) dataset with 110 participants (55 depressed patients and 55 normal controls) collected during emotional tasks with four affective polarities (positive, neutral, negative, and resting state). Our analyses revealed oscillation-dependent microstates that reflected abnormalities in the networks associated with external information processing and interoception in depression. Our analyses further suggest that such abnormalities may be caused by dysfunction in a limited number of brain regions, which then dynamically affects functional brain networks. These findings may reflect increased self-focus and deficits in the perception of external information in depression. In summary, our study further explores source-level evidence related to abnormalities in microstate features of depression based on the generalization of depression microstate research to a multi-band framework. Our study provides a direction for expanding the application of dynamic reorganization analyses of functional networks with high temporal resolution, and provides new support and insights into the neural mechanisms of affective dysfunction in depression.