A common model of power supply for implantable devices was established to study factors affecting volume conduction energy transfer. Electromagnetic and equivalent circuit models were constructed to study the effect o...A common model of power supply for implantable devices was established to study factors affecting volume conduction energy transfer. Electromagnetic and equivalent circuit models were constructed to study the effect of separation between the source electrode pairs on volume conduction energy transfer. In addition, the parameters of external signal including waveform, amplitude and frequency were analyzed. As the current amplitude did not lead to tissue injury and the current frequency did not cause nerve excitability, the recommended separation be- tween the source electrodes was 3 cm, the proposed waveform of signal source was sinusoidal wave and the opti- mal frequency was 200 KHz. In agar experiment and swine skin experiment, the current transfer efficiencies were 28.13% and 20.65%, respectively, and the energy transfer efficiencies were 9.86% and 6.90%, respectively. In conclusion, we can achieve optimal efficiency of energy transfer by appropriately setting the separation between the source electrode parameters of the signal source.展开更多
Subject-independent Electroencephalography(EEG) recognition remains challenging due to inherent variability of brain anatomy across different subjects. Such variability is further complicated by the Volume Conduction ...Subject-independent Electroencephalography(EEG) recognition remains challenging due to inherent variability of brain anatomy across different subjects. Such variability is further complicated by the Volume Conduction Effect(VCE) that introduces channel-interference noise, exacerbating subject-specific biases in the recorded EEG signals. Existing studies, often relying large datasets and entangled spatial-temporal features, struggle to overcome this bias,particularly in scenarios with limited EEG data. To this end, we propose a Temporal-Connective EEG Representation Learning(TCERL) framework that disentangles temporal and spatial feature learning. TCERL first employs an one-dimensional convolutional network to extract channel-specific temporal features, mitigating channel-interference noise caused by VCE. Building upon these temporal features, TCERL then leverages Graph Neural Networks to extract subject-invariant topological features from a functional brain network, constructed using the channel-specific features as nodes and functional connectivity as the adjacency matrix.This approach allows TCERL to capture robust representations of brain activity patterns that generalize well across different subjects. Our empirical experiment demonstrates that TCERL outperforms state-of-the-art across a range of training subjects on four public benchmarks and is less sensitive to subject variability. The performance gain is highlighted when limited subjects are available, suggesting the robustness and transferability of the proposed method. Source code can be found in: https://github.com/haoweilou/TCERL.展开更多
文摘A common model of power supply for implantable devices was established to study factors affecting volume conduction energy transfer. Electromagnetic and equivalent circuit models were constructed to study the effect of separation between the source electrode pairs on volume conduction energy transfer. In addition, the parameters of external signal including waveform, amplitude and frequency were analyzed. As the current amplitude did not lead to tissue injury and the current frequency did not cause nerve excitability, the recommended separation be- tween the source electrodes was 3 cm, the proposed waveform of signal source was sinusoidal wave and the opti- mal frequency was 200 KHz. In agar experiment and swine skin experiment, the current transfer efficiencies were 28.13% and 20.65%, respectively, and the energy transfer efficiencies were 9.86% and 6.90%, respectively. In conclusion, we can achieve optimal efficiency of energy transfer by appropriately setting the separation between the source electrode parameters of the signal source.
文摘Subject-independent Electroencephalography(EEG) recognition remains challenging due to inherent variability of brain anatomy across different subjects. Such variability is further complicated by the Volume Conduction Effect(VCE) that introduces channel-interference noise, exacerbating subject-specific biases in the recorded EEG signals. Existing studies, often relying large datasets and entangled spatial-temporal features, struggle to overcome this bias,particularly in scenarios with limited EEG data. To this end, we propose a Temporal-Connective EEG Representation Learning(TCERL) framework that disentangles temporal and spatial feature learning. TCERL first employs an one-dimensional convolutional network to extract channel-specific temporal features, mitigating channel-interference noise caused by VCE. Building upon these temporal features, TCERL then leverages Graph Neural Networks to extract subject-invariant topological features from a functional brain network, constructed using the channel-specific features as nodes and functional connectivity as the adjacency matrix.This approach allows TCERL to capture robust representations of brain activity patterns that generalize well across different subjects. Our empirical experiment demonstrates that TCERL outperforms state-of-the-art across a range of training subjects on four public benchmarks and is less sensitive to subject variability. The performance gain is highlighted when limited subjects are available, suggesting the robustness and transferability of the proposed method. Source code can be found in: https://github.com/haoweilou/TCERL.
