Dong Wen

Open-source software platforms have revolutionized brain-computer interface (BCI) development. This comprehensive review examines the ecosystem for non-invasive EEG-based BCIs through four dimensions: technological foundations, tool taxonomy, scenario adaptation, and future evolution. The study demonstrates how open-source software enables complete BCI workflows from signal acquisition to real-time interaction. A systematic framework categorizes tools for applications in healthcare, education, and entertainment. Technical challenges and breakthrough opportunities in the ecosystem are identified. The study offers developers methodologies to select open-source BCI tools. Collectively, these insights aim at accelerating the transition of BCI technologies from research settings to real-world deployment.

The implementation of surface electromyography (sEMG)-based hand gesture recognition on mobile and wearable systems is frequently restricted by the finite computing, memory, and battery capabilities of edge devices. Even though a low-density sEMG setup is a feasible hardware implementation, achieving robust recognition under such constraint conditions becomes very challenging due to the non-stationary nature and inter-subject variance. In this paper, we propose DSCAttenEMG, an efficient neural network that combines both Depthwise Separable Convolution (DSC) for local feature extraction and Multi-Head Self-Attention (MHSA) to model long-range dependencies on EMG/IMU data, using 1× 1 DSC followed by Global Average Pooling to replace high-dimensional fully connected layers. Extensive experimentation on a self-collected dataset, the public SeNic and BandMyo datasets shows that our approach achieves state-of-the-art recognition performance (94.45%, 94.11% and 92.89%) at negligible complexity (only 178–179K parameters). The model is capable of real-time inference (0.93ms on RTX 4090 GPU, 6.68ms on NVIDIA Jetson AGX Orin, 1.4/0.7ms on CPU/NPU of Qualcomm mobile platform) and has a high degree of practicality for embedded deployment (118 samples/s at $\sim$1 W on K230 edge AI platform). This amalgamation of three pivotal strengths, elevated accuracy, enhanced efficiency, and pragmatic viability, highlights its substantial potential for practical mobile and wearable applications.

Triboelectric nanogenerators (TENGs) offer significant potential for mechanical energy harvesting, but their development is constrained by the trade-off between high charge trapping capability and adequate electrical conductivity, which limits energy conversion efficiency. To tackle this issue, a dual-modification strategy involving the incorporation of polytetrafluoroethylene (PTFE) and multi-walled carbon nanotubes (MWCNTs) into polydimethylsiloxane (PDMS) was developed. PTFE was introduced to enhance charge trapping through its strong electron affinity, while MWCNTs formed a conductive network to facilitate rapid charge transport. Under optimized doping concentrations of 5 wt.% PTFE and 0.25 wt.% MWCNTs, the composite exhibited substantial performance enhancements, with open-circuit voltage, short-circuit current, and transferred charge increased by 294.43%, 521.0%, and 252.55%, respectively, compared to pure PDMS. A peak power density of 8.37 W/m² was achieved under a 20 MΩ load. The composite membrane exhibited a tensile strength of 4.21 ± 0.42 MPa, elastic modulus of 1.45 ± 0.07 MPa, and elongation at break of 169.80 ± 18.69%. The device displayed excellent thermal stability (20-100 °C) and slight UV-enhanced output, despite degradation at high humidity, it successfully retained 70.76% of its initial voltage at moderate humidity levels (55-65% RH). It maintained stable electrical output after 5000 seconds of continuous operation under mechanical stress, and generated clear voltage signals during various human motions such as walking, running, and jumping. Additionally, our motion recognition model based on deep learning techniques attained a high accuracy of 99.95%, demonstrating its effectiveness in action classification tasks. Combined with a UI visualization interface, it enabled the assessment of motion energy consumption. This study demonstrates an effective material design strategy for improving TENG performance and provides valuable insights for the advancement of high-efficiency wearable energy harvesting and information fusion systems.

Spatial cognition, a critical component of human cognitive function, can be enhanced through targeted training, such as virtual reality (VR)-based interventions. Recent advances in electroencephalography (EEG)-based functional connectivity analysis have highlighted the importance of network topology features for understanding cognitive processes. In this paper, a framework based on a cross fuzzy entropy network graph (CFENG) is proposed to extract spatial cognitive EEG network topological features. This framework involves calculating the similarity and symmetry between EEG channels using cross fuzzy entropy, constructing weighted directed network graphs, transforming one-dimensional EEG signals into two-dimensional brain functional connectivity networks, and extracting both local and global topological features. The model’s performance is evaluated and interpreted using an XGBoost classifier. Experiments on an EEG dataset from group spatial cognitive training validated the CFENG model. In the Gamma band, the CFENG achieved 97.82% classification accuracy, outperforming existing methods. Notably, the asymmetrically distributed EEG channels Fp1, P8, and Cz contributed most to spatial cognitive signal classification. An analysis after 28 days of training revealed that specific VR games enhanced functional centrality in spatial cognition-related brain regions, reduced information flow path length, and altered information flow symmetry. These findings support the feasibility of VR-based spatial cognitive training from a brain functional connectivity perspective.

In the field of brain-computer interface, steady-state visual evoked potential (SSVEP) is widely used because of its stability. Although high-intensity stimulus has good accuracy, it can cause severe visual fatigue and even induce epilepsy in subjects. As well as paying attention to its accuracy, personnel should also pay attention to the comfort of the subject. In this paper, combined with the knowledge of spatial psychology, the overlooking map is proposed as the stimulus source to induce the SSVEP signal. 24 subjects participated in the comparison experiment between the overlooking map and the black and white stimuli, and the EEG signal was processed and analyzed online in real time through the CCA algorithm. Afterwards, the two stimuli were scored in terms of personal preference, comfort, and flickering sensation. The experimental results show that the performance of the overlooking map stimulus source is superior to that of the black and white stimulus, and it is more suitable to induce the SSVEP signal of the subjects. As an important aspect of SSVEP-based application and a necessary factor for commercial promotion, user experience provides a good theoretical and experimental research basis for it.