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Survey of EEG Neurofeedback methods for Attention Improvement
Hyunji Kim, Daeun Gwon, Kyungho Won, Sung Chan Jun, Minkyu Ahn
http://doi.org/10.5626/JOK.2021.48.10.1105
Neurofeedback is a method through which a user self-regulates the brain state using the feedback of his/her own brain signal. This can be used to restore or improve brain functions. In this study, we reviewed 108 articles on electroencephalogram (EEG) neurofeedback for attention improvement and surveyed the important parameters. As a result, we found that most studies were conducted with patient subjects and mostly brain signals were recorded from central areas on the scalp by using wet and wire EEG systems. Sensory-motor-rhythm or the ratio between theta and low beta rhythms were used as attention index, and this information was provided to users through auditory or visual stimuli. In addition, Continuous Performance Test or Go/NoGo test was employed for behavior evaluation. Based on these results, we suggest the following directions for the further advancement of the practical neurofeedback system; the future work should target non-patient subjects and utilize wireless/dry EEG devices and virtual/augmented reality for increasing user convenience and building more immersive application. Lastly, a standard or guide for developing usable neurofeedback applications should be established.
A Method for Cancer Prognosis Prediction Using Gene Embedding
http://doi.org/10.5626/JOK.2021.48.7.842
Identifying prognostic genes and using them to predict the prognosis of cancer patients can help provide them with more effective treatments. Many methods have been proposed to identify prognostic genes and predict cancer prognosis, and recent studies have focused on machine learning methods including deep learning. However, applying gene expression data to machine learning methods has the limitations of a small number of samples and a large number of genes. In this study, we additionally use a gene network to generate many random gene paths, which we used for training the model, thereby compensating for the small sample problem. We identified the prognostic genes and predicted the prognosis of patients using the gene expression data and gene networks for five cancer types and confirmed that the proposed method showed better predictive accuracy compared to other existing methods, and good performance on small sample data.
Semantic Similarity-based Intent Analysis using Pre-trained Transformer for Natural Language Understanding
Sangkeun Jung, Hyein Seo, Hyunji Kim, Taewook Hwang
http://doi.org/10.5626/JOK.2020.47.8.748
Natural language understanding (NLU) is a central technique applied to developing robot, smart messenger, and natural interface. In this study, we propose a novel similarity-based intent analysis method instead of the typical classification methods for intent analysis problems in the NLU. To accomplish this, the neural network-based text and semantic frame readers are introduced to learn semantic vectors using pairwise text-semantic frame instances. The text to vector and the semantic frame to vector projection methods using the pre-trained transformer are proposed. Then, we propose a method of attaching the intention tag of the nearest training sentence to the query sentence by measuring the semantic vector distances in the vector space. Four experiments on the natural language learning suggest that the proposed method demonstrates superior performance compared to the existing intention analysis techniques. These four experiments use natural language corpora in Korean and English. The two experiments in Korean are weather and navigation language corpora, and the two English-based experiments involve air travel information systems and voice platform language corpora.
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