Many forms of Intangible Culture Heritage (ICH) are fast disappearing. The Lantern Festival is an important part of Chinese tradition and a critical ICH of Taiwan. However, few young people attend ICH activities. Given the importance of ICH education, this study describes what participants learned after lantern exhibitions that combined Augmented Reality (AR), mobile websites, and Paper-based Exhibition Brochures. This study evaluated what a cohort of college students (n = 115) learned about ICH through experiments and questionnaires. The results show that the public understood ICH better through digital technology. All participants thought that applying AR to ICH was good for the public to gain more information on lantern culture, although it took a lot of time to learn the new technology. The results also encouraged the application of AR to engage in ICH education. Therefore, a large number of digital technologies that are popular among young people should be used as part of ICH education in order to improve their cultural awareness and learning motivation. 

Design and Evaluation for Improving Lantern Culture Learning Experience with Augmented Reality

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.

pdf/self-supervised-learning-for-electroencephalogram-a-w7of0g276i.pdf

Self-supervised Learning for Electroencephalogram: A Systematic Survey

A robust operators’ cognitive workload recognition method based on denoising masked autoencoder

Shadow play is a traditional culture to communicate narratives. However, its inheritance is in danger, and traditional methods have limitations in their expression. We propose a novel system to perform a shadow play by levitating props using an ultrasound speaker array. Our system computes an ideal position of levitating props to create a shadow of the desired image. Shadow play will be performed by displaying a sequence of images as shadows. Since the performance is automated, our work makes shadow play accessible to people for generations. Also, our system allows 6 DoF and floating movement of props, which expands the limit of expression. Through this system, we aim to enhance shadow plays informatically and aesthetically.

Shadow Play using Ultrasound Levitated Props

Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative representations. To address these challenges, we introduce \textit{EEG2Rep}, a self-prediction approach for self-supervised representation learning from EEG. Two core novel components of EEG2Rep are as follows: 1) Instead of learning to predict the masked input from raw EEG, EEG2Rep learns to predict masked input in latent representation space, and 2) Instead of conventional masking methods, EEG2Rep uses a new semantic subsequence preserving (SSP) method which provides informative masked inputs to guide EEG2Rep to generate rich semantic representations. In experiments on 6 diverse EEG tasks with subject variability, EEG2Rep significantly outperforms state-of-the-art methods. We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50\% of EEG recordings will result in the most accurate results on all 6 tasks on average. Finally, we show that EEG2Rep is robust to noise addressing a significant challenge that exists in EEG data. Models and code are available at:\url{https://github.com/Navidfoumani/EEG2Rep} 

pdf/eeg2rep-enhancing-self-supervised-eeg-representation-through-20d70tv9tf.pdf

EEG2Rep: Enhancing Self-supervised EEG Representation Through
  Informative Masked Inputs

Abstract We developed an intelligent shadow play system (ShadowTouch) based on multi-dimensional interactive perception technologies to let user interact with the shadow play figure naturally. ShadowTouch included a shadow play hardware subsystem and a multi-dimensional interactive software subsystem. In shadow play hardware subsystem, each joint of the shadow play figure was actuated by the connecting rod which was rotated through one steering gear. In multi-dimensional interactive software subsystem, a somatosensory interaction module synchronized motions between shadow play figure and human body based on skeleton point detection, and an automatic choreography module generated the motions automatically for the shadow play figure based on musical emotion recognition. The proposed system improved the immersive interaction experience and let users feel involved during the performance of shadow play, and the motions of shadow play figure (steering gear motion angles) could be saved digitally to further be applied for the reactivation and inheritance of traditional culture.

An Intelligent Shadow Play System With Multi-dimensional Interactive Perception

Affective computing based on electroencephalogram (EEG) has been widely studied due to the high correlation between the brain and emotions. Neurological research shows that emotions are generated by the interaction of neural circuits separated in different brain regions. Recently, many deep learning methods have been demonstrated to recognize different emotions. However, these methods extract features at the data level, ignoring the brain’s neural mechanisms of emotion generation, which may result in the features’ weak ability to express emotion. To solve this problem and extract more discriminant features according to the neural mechanism of emotion generation, this article proposes an attention based spatial-temporal representation method (ASTR) for emotion recognition. The ASTR method constructs spatial-temporal representations of emotion from neural circuits’ activity in different brain regions. In the model, the cascade attention quantifies the multi-granularity correlation and information exchange pattern between brain regions as the spatial features, which measure neural circuits’ activation and information interaction in different brain regions. The one-way transformer introduces temporal order in spatial features, calculates temporal variation features from the long-term activity of neural circuits and generates the emotion representation. The representation incorporates spatial-temporal features of neural circuits’ activity during emotion generation, which is highly related to emotion. We conducted experiments on SEED and DEAP datasets. The experimental results show that the emotion recognition accuracy of our model is about 3.0% higher than that of the SOTA algorithms, reaching more than 98%. The results also visualize the emotion generation process, revealing how the brain works under different emotions. 

An Efficient Spatial-Temporal Representation Method for EEG Emotion Recognition

Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by the high cost and significant label discrepancies to manually label large-scale EEG datasets. Self-supervised frameworks are adopted in vision and language fields to solve this issue, but the lack of EEG-specific theoretical foundations hampers their applicability across various tasks. To solve these challenges, this paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2), which integrates neurological theory to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. Sequentially, inter-view and cross-view contrastive learning pipelines in combination with various augmentation methods are applied to capture neural features from different views. By modeling prior neural knowledge based on homologous neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations. Experimental results on different downstream tasks demonstrate that our method outperforms state-of-the-art methods, highlighting the superior generalization of neural knowledge-supported EEG representations across various brain tasks.

pdf/a-knowledge-driven-cross-view-contrastive-learning-for-eeg-2e3tbpmgt0.pdf

A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation

Semantic-oriented Visual Prompt Learning for Class Incremental Learning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Weining Weng

Author Tools

Chat about Author