From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

Denoising EEG Signals for Real-World BCI Applications Using GANs.

As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals.

AuNP-DG: deoxyglucose-labeled gold nanoparticles as X-ray computed tomography contrast agents for cancer imaging.

PURPOSE: To study the feasibility of using 2-deoxy-D-glucose (2-DG)-labeled gold nanoparticle (AuNP-DG) as a computed tomography (CT) contrast agent with tumor targeting capability through in vitro experiments. PROCEDURES: Gold nanoparticles (AuNP) were fabricated and were conjugated with 2-deoxy-D-glucose. The human alveolar epithelial cancer cell line, A-549, was chosen for the in vitro cellular uptake assay. Two groups of cell samples were incubated with the AuNP-DG and the unlabeled AuNP, respectively. Following the incubation, the cells were washed with sterile PBS to remove the excess gold nanoparticles and spun to cell pellets using a centrifuge. The cell pellets were imaged using a microCT scanner immediately after the centrifugation. The reconstructed CT images were analyzed using a commercial software package. RESULTS: Significant contrast enhancement in the cell samples incubated with the AuNP-DG with respect to the cell samples incubated with the unlabeled AuNP was observed in multiple CT slices. CONCLUSIONS: Results from this study demonstrate enhanced uptake of 2-DG-labeled gold nanoparticle by cancer cells in vitro and warrant further experiments to study the exact molecular mechanism by which the AuNP-DG is internalized and retained in the tumor cells.

Photovoltage mechanism for room light conversion of citrate stabilized silver nanocrystal seeds to large nanoprisms.

We investigate the photoconversion of aqueous 8 nm Ag nanocrystal seeds into 70 nm single crystal plate nanoprisms. The process relies on the excitation of Ag surface plasmons. The process requires dioxygen, and the transformation rate is first-order in seed concentration. Although citrate is necessary for the conversion, and is consumed, the transformation rate is independent of citrate concentration. We propose a mechanism that accounts for these features by coupling the oxidative etching of the seed and the subsequent photoreduction of aqueous Ag(+). The reduced Ag deposits onto a Ag prism of specific size that has a cathodic photovoltage resulting from plasmon "hot hole" citrate photo-oxidation. This photovoltage mechanism also explains recent experimental results involving single and dual wavelength irradiation and the core/shell synthesis of Ag layers on Au seeds.

Crystal structure and the paraelectric-to-ferroelectric phase transition of nanoscale BaTiO3.

We have investigated the paraelectric-to-ferroelectric phase transition of various sizes of nanocrystalline barium titanate (BaTiO3) by using temperature-dependent Raman spectroscopy and powder X-ray diffraction (XRD). Synchrotron X-ray scattering has been used to elucidate the room temperature structures of particles of different sizes by using both Rietveld refinement and pair distribution function (PDF) analysis. We observe the ferroelectric tetragonal phase even for the smallest particles at 26 nm. By using temperature-dependent Raman spectroscopy and XRD, we find that the phase transition is diffuse in temperature for the smaller particles, in contrast to the sharp transition that is found for the bulk sample. However, the actual transition temperature is almost unchanged. Rietveld and PDF analyses suggest increased distortions with decreasing particle size, albeit in conjunction with a tendency to a cubic average structure. These results suggest that although structural distortions are robust to changes in particle size, what is affected is the coherency of the distortions, which is decreased in the smaller particles.

Photoinduced thermal copper reduction onto gold nanocrystals under potentiostatic control.

We study the photoreduction of adsorbed copper ions onto Au nanoparticles, on an indium tin oxide (ITO) electrode in an aqueous electrochemical cell, as a function of applied voltage and laser intensity. The photocurrent is a nonlinear function of laser intensity and increases sharply with cathodic voltage in the underpotential deposition region. The photoreduction is attributed to laser heating of the Au nanoparticles rather than "hot electron" processes. Numerical simulation of the Butler-Volmer kinetic equation using experimental parameters predicts a several orders of magnitude increase in current for a temperature rise of a few Kelvin.

Electrochemical ostwald ripening of colloidal ag particles on conductive substrates.

Thermally evaporated silver nanoparticles on conducting substrates spontaneously evolve in size when immersed in pure water. The process was studied using scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDX), and optical absorption spectroscopy. The particles are proposed to reform through an electrochemical Ostwald ripening mechanism driven by the size dependence of the work function and standard electrode potential. We also discuss prior literature experiments where this process appears to occur. Our results show the sensitivity of the electrochemical properties of metallic nanoparticles at relatively large sizes (approximately 50 nm).