2019 7th International Winter Conference on Brain-Computer Interface (BCI)最新文献_第4页

A study on reducing training time of BCI system based on an SSVEP dynamic model 基于SSVEP动态模型的脑机接口系统训练时间缩短研究

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737318

Xu Han, Shangen Zhang, Xiaorong Gao

引用次数: 5

Neural manifolds: from basic science to practical improvements in brain-computer intefaces 神经流形:从基础科学到脑机接口的实际改进

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737339

S. Chase

引用次数: 1

Immediate effect of neurofeedback in passive BCI for alertness control 被动脑机接口中神经反馈对警觉性控制的即时效应

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737325

A. Hramov, V. Maksimenko, M. Zhuravlev, A. Pisarchik

引用次数: 2

BCI 2019 Author Index BCI 2019作者索引

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/iww-bci.2019.8737324

引用次数: 1

EZSL-GAN: EEG-based Zero-Shot Learning approach using a Generative Adversarial Network EZSL-GAN:使用生成对抗网络的基于脑电图的零射击学习方法

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737322

Sunhee Hwang, Kibeom Hong, G. Son, H. Byun

{"title":"EZSL-GAN: EEG-based Zero-Shot Learning approach using a Generative Adversarial Network","authors":"Sunhee Hwang, Kibeom Hong, G. Son, H. Byun","doi":"10.1109/IWW-BCI.2019.8737322","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737322","url":null,"abstract":"Recent studies show that deep neural network can be effective for learning EEG-based classification network. In particular, Recurrent Neural Networks (RNN) show competitive performance to learn the sequential information of the EEG signals. However, none of the previous approaches considers recognizing the unknown EEG signals which have never been seen in the training dataset. In this paper, we first propose a new scheme for Zero-Shot EEG signal classification. Our EZSL-GAN has three parts. The first part is an EEG encoder network that generates 128-dim of EEG features using a Gated Recurrent Unit (GRU). The second part is a Generative Adversarial Network (GAN) that can tackle the problem for recognizing unknown EEG labels with a knowledge base. The third part is a simple classification network to learn unseen EEG signals from the fake EEG features which are generated from the learned Generator. We evaluate our method on the EEG dataset evoked from 40 classes visual object stimuli. The experimental results show that our EEG encoder achieves an accuracy of 95.89%. Furthermore, our Zero-Shot EEG classification method reached an accuracy of 39.65% for the ten untrained EEG classes. Our experiments demonstrate that unseen EEG labels can be recognized by the knowledge base.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121057859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Testing performance of multicolour checkerboard flickers against their greyscale versions for SSVEP-based BCI 针对基于ssvep的BCI的灰度版本测试多色棋盘闪烁的性能

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737261

A. Szalowski, D. Picovici

引用次数: 2

Transparent electroencephalography? : Exploring ear-EEG for long-term, mobile electrophysiology 透明的脑电图吗?探索耳-脑电图的长期活动电生理

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737309

S. Debener, M. Bleichner

引用次数: 3

Biometrics Based on Single-Trial EEG 基于单次脑电图的生物识别

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737254

G. Choi, Soo-In Choi, Rahmawati Rahmawati, Hyung-Tak Lee, Yun-Sung Lee, Seong-Uk Kim, Han-Jeong Hwang

{"title":"Biometrics Based on Single-Trial EEG","authors":"G. Choi, Soo-In Choi, Rahmawati Rahmawati, Hyung-Tak Lee, Yun-Sung Lee, Seong-Uk Kim, Han-Jeong Hwang","doi":"10.1109/IWW-BCI.2019.8737254","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737254","url":null,"abstract":"The biometrics based on resting state electroencephalography (EEG) is better than other EEG-based authentication protocols in terms of usability because it does not require any external stimuli and has a relatively short authentication time. Most of previous resting state EEG-based authentication systems have used a relatively long EEG data (e.g., > 1 min) measured once, and they were segmented to create many trials (e.g., > 100). In this case, however, it is difficult to reflect real-authentication situations in which a user repetitively uses an authentication system in different time points. Therefore, we propose to use single trials repetitively measured for short time (10 s). In the experiment, resting state EEGs were measured while fifteen subjects opened and closed their eyes 30 times for 10 s each. The measured EEG data were divided into three conditions, which are eyes open (EO), eyes closed (EC), and difference between EC and EO (Diff). We extracted power spectral density (PSD) ranging from 3 to 20 Hz as features for classification, with which a binary classification based on a 5×5-fold cross-validation was performed for each subject using linear discriminant analysis (LDA). The mean authentication accuracies of EC, EO, and Diff were 97.05 ± 5.4, 92.5 ± 8.2, and 85.3 ± 7.0 %, respectively, demonstrating the feasibility of single-trial-based EEG authentication. EC could be an optimal condition for developing a resting-state EEG authentication system based on single trial.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114228878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

[Copyright notice] (版权)

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/iww-bci.2019.8737313

引用次数: 0

The Effect of Neurofeedback Training in Virtual and Real Environments based on BCI 基于脑机接口的虚拟和真实环境下神经反馈训练的效果

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737323

Dong-Kyun Han, Min-Ho Lee, J. Williamson, Seong-Whan Lee

引用次数: 3