ABSTRACT
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.
Subject(s)
Algorithms , Brain-Computer Interfaces , Electroencephalography , Evoked Potentials, Visual , Humans , Evoked Potentials, Visual/physiology , Male , Adult , Female , Neural Networks, Computer , Young Adult , Calibration , Reproducibility of ResultsABSTRACT
Highly sensitive, simple and reliable colorimetric probe for Cu2+-ion detection was visualized with the L-cysteine functionalized gold nanoparticle (LS-AuNP) probes. The pronounced sensing of Cu2+ with high selectivity was rapidly featured with obvious colour change that enabled to visually sense Cu2+ ions by naked eyes. By employing systemic investigations on crystallinities, elemental compositions, microstructures, surface features, light absorbance, zeta potentials and chemical states of LS-AuNP probes, the oxidation-triggered aggregation effect of LS-AuNP probes was envisioned. The results indicated that the mediation of Cu2+ oxidation coordinately caused the formation of disulfide cystine, rendering the removal of thiol group at AuNPs surfaces. These features reflected the visual colour change for the employment of tracing Cu2+ ions in a quantitative way.
ABSTRACT
In this work, a high-density hydrogen (HDH) treatment is proposed to reduce interface traps and enhance the efficiency of the passivated emitter rear contact (PERC) device. The hydrogen gas is compressed at pressure (~ 70 atm) and relatively low temperature (~ 200 °C) to reduce interface traps without changing any other part of the device's original fabrication process. Fourier-transform infrared spectroscopy (FTIR) confirmed the enhancement of Si-H bonding and secondary-ion mass spectrometry (SIMS) confirmed the SiN/Si interface traps after the HDH treatment. In addition, electrical measurements of conductance-voltage are measured and extracted to verify the interface trap density (Dit). Moreover, short circuit current density (Jsc), series resistance (Rs), and fill factor (F.F.) are analyzed with a simulated light source of 1 kW M-2 global AM1.5 spectrum to confirm the increase in cell efficiency. External quantum efficiency (EQE) is also measured to confirm the enhancement in conversion efficiency between different wavelengths. Finally, a model is proposed to explain the experimental result before and after the treatment.
