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The results were statistically analysed in terms of their Pearson's correlation coefficients, root-mean-square errors, and signal-to-noise ratios.

As expected, the wavelet implementation choices greatly influenced the processing performance. Overall, the Haar wavelet with a 5-level decomposition, hard thresholding method, and the threshold proposed by Han et al. (2007) achieved the best outcomes. Based on the adopted performance metrics, wavelet denoising with these parametrizations outperformed conventional 300-3000 Hz linear bandpass filtering.

These results can be used to guide the reasoned and accurate selection of wavelet denoising implementation choices in the context of neural signal processing, particularly when spike-morphology preservation is required.

These results can be used to guide the reasoned and accurate selection of wavelet denoising implementation choices in the context of neural signal processing, particularly when spike-morphology preservation is required.

Decoding language representations directly from the brain can enable new Brain-Computer Interfaces (BCI) for high bandwidth human-human and human-machine communication. Clinically, such technologies can restore communication in people with neurological conditions affecting their ability to speak.

In this study, we propose a novel deep network architecture Brain2Char, for directly decoding text (specifically character sequences) from direct brain recordings (called Electrocorticography, ECoG). Brain2Char framework combines state-of-the-art deep learning modules --- 3D Inception layers for multiband spatiotemporal feature extraction from neural data and bidirectional recurrent layers, dilated convolution layers followed by language model weighted beam search to decode character sequences, optimizing a connectionist temporal classification (CTC) loss. Additionally, given the highly non-linear transformations that underlie the conversion of cortical function to character sequences, we perform regularizations on the network's latent representations motivated by insights into cortical encoding of speech production and artifactual aspects specific to ECoG data acquisition. To do this, we impose auxiliary losses on latent representations for articulatory movements, speech acoustics and session specific non-linearities.

In 3 (out of 4) participants reported here, Brain2Char achieves 10.6%, 8.5% and 7.0%Word Error Rates (WER) respectively on vocabulary sizes ranging from 1200 to 1900 words.

These results establish a new end-to-end approach on decoding text from brain signals and demonstrate the potential of Brain2Char as a high-performance communication BCI.

These results establish a new end-to-end approach on decoding text from brain signals and demonstrate the potential of Brain2Char as a high-performance communication BCI.Electrical neurostimulation is an increasingly adopted therapeutic methodology for neurological conditions such as epilepsy. Electrical neurostimulation devices are commonly characterized by their limited sensing, actuating, and computational capabilities. However, the sensing mechanisms are often used only for their detection potential (e.g., to detect seizures), which automatically and dynamically trigger the actuation capabilities, but ultimately deploy prespecified stimulation doses that resulted from a period of manual (and empirical) calibration. The potential information contained in the measurements acquired by the sensing mechanisms is, therefore, considerably underutilized, given that this type of stimulation strategy only entails an event-triggered relationship between the sensors and actuators of the device. Such stimulation strategies are suboptimal in general and lack theoretical guarantees regarding their performance. In order to leverage the aforementioned information, harvested during normal ion by computational simulation of our proposed strategy upon seizure-like events. Lastly, we provide evidence of the effectiveness of our method on seizures simulated by commonly adopted models in the neuroscience and medical community present in the literature, as well as real seizure data as obtained from subjects with epilepsy.Assessment of radiation absorbed dose to internal organs of the body from the intake of radionuclides, or in the medical setting through the injection of radiopharmaceuticals, is generally performed based upon reference biokinetic models or patient imaging data, respectively. Biokinetic models estimate the time course of activity localized to source organs. Rilematovir chemical structure The time-integration of these organ activity profiles are then scaled by the radionuclide S-value, which defines the absorbed dose to a target tissue per nuclear transformation in various source tissues. S-values are computed using established nuclear decay information (particle energies and yields), and a parameter termed the specific absorbed fraction (SAF). The SAF is the ratio of the absorbed fraction-fraction of particle energy emitted in the source tissue that is deposited in the target tissue-and the target organ mass. While values of the SAF may be computed using patient-specific or individual-specific anatomic models, they have been more widely avor source-target combinations at large intra-organ separation distances. Based on these analyses, various data smoothing algorithms were employed, including multi-point weighted data smoothing, and log-log interpolation at low energies (1 keV and 5 keV) using limiting SAF values based upon target organ mass to bound the interpolation interval. The final dataset is provided in a series of ten electronic supplemental files in MS Excel format. The results of this study were further used as the basis for assessing the radiative component of internal electron source SAFs as described in our companion paper (Schwarz et al 2021) for this same pediatric phantom series.Due to its extraordinary properties, graphene has been widely used as reinforcing nanofillers to enhance the mechanical properties of polymer- or metal-based composites. However, the weak interfacial interaction between the matrix and graphene is still a major bottleneck that considerably hinders its reinforcing effectiveness and efficiency. This study presents an atomistic study via molecular dynamics simulation on a chemical modification strategy where the aluminium (Al) substrate is modified with Al2O3 (with or without covalent bonds formed between Al2O3 and graphene) or Al4C3 to achieve significantly improved interfacial shear strength and overall mechanical properties of graphene-reinforced aluminium (Al/Gr) composites. Numerical results show that this strategy works very well and among the three cases considered, modifying Al substrate by Al2O3 without covalent bonds formed at the interface between Al2O3 and graphene produces the strongest interfacial interaction and the best mechanical properties. In the presence of covalent bonds, however, the reinforcing effect is adversely affected due to the sp2-sp3 bond transformation which partially degrades graphene.