The Blockchain technology moves data nodes and data streams from one informatics center to another based on the importance of Bitcoins, resulting in a dedicated, public, and secure network for interpretation and devel...
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Election is a basic democratic tool that offers an official mechanism for the people to express their opinions in order to form a government by democratic means. Electronic voting has evolved into the most significant...
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Office work presents health and wellbeing challenges, triggered by working habits or environmental factors. While technological interventions gain popularity in the workplace, they often fall short of acknowledging pe...
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Advancements in 3D rendering like Gaussian Splatting (GS) allow novel view synthesis and real-time rendering in virtual reality (VR). However, GS-created 3D environments are often difficult to edit. For scene enhancem...
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We present the design and evaluation of the Movits, a minimalist toolkit for embodied sketching design explorations. The toolkit includes technology probes featuring minimalist wearable digital units that support the ...
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Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-Com...
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ISBN:
(数字)9781665410205
ISBN:
(纸本)9781665410212
Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about x 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.
Objective: Low intensity focused stimulation (LIFUS) has been proved to improve motor function in Parkinson's disease (PD) animal modules. The aim of this study is to investigate whether LIFUS target on the primar...
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Spatial memory supported by the hippocampus is a critical cognitive function in daily life. It helps us to navigate to salient goal locations. Hippocampal place cells stably represent the spatial location, while c-Fos...
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Novelty recognition is essential for exploring and collecting unknown but interesting information, in which multiple brain regions related to cognitive memory are involved. Dorsal hippocampus (dHPC) is revealed to be ...
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