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

Brain-to-brain interface increases efficiency of human-human interaction 脑对脑接口提高了人与人互动的效率

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737316

V. Maksimenko, A. Hramov, A. Runnova, A. Pisarchik

引用次数: 2

Quantification of Motion Artifacts in fNIRS Data by Monitoring Sensor Attachment 利用监测传感器附件量化fNIRS数据中的运动伪影

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737342

Jinwoo Park, Sunghee Dong, Yuseong Hong, Jichai Jeong

引用次数: 1

High engagement in BCI action observation game by relevant character’s movement 通过相关角色的动作，在BCI动作观察游戏中具有较高的参与度

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737252

Hyunmi Lim, J. Ku

引用次数: 2

A SLAM Integrated Hybrid Brain-Computer Interface for Accurate and Concise Control SLAM集成混合脑机接口，实现精确、简洁的控制

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737331

Junyong Park, Jin Woo Choi, Sungho Jo

引用次数: 2

Development of Brain Computer Interface based Action Observation Program with Functional Electrical Stimulation device(FES) 基于脑机接口的功能电刺激装置(FES)动作观察程序开发

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737253

J. Son, J. Ku

引用次数: 2

P300-based deception detection of mock network fraud with modified genetic algorithm and combined classification 基于p300的改进遗传算法和组合分类模拟网络欺诈检测

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737320

Xiaochen Liu, Ji-zhong Shen, Wufeng Zhao

{"title":"P300-based deception detection of mock network fraud with modified genetic algorithm and combined classification","authors":"Xiaochen Liu, Ji-zhong Shen, Wufeng Zhao","doi":"10.1109/IWW-BCI.2019.8737320","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737320","url":null,"abstract":"To detect network fraud, a three-stimulus paradigm was used in a mock crime P300-based concealed information test. A P300-based deception detection method based on a modified genetic algorithm and a confidence-coefficient-based combined classifier was created for mock network fraud detection. After the multi-domain integrated signal preprocessing and feature extraction, a modified logistic equation based multi-population genetic algorithm was adopted for feature selection to obtain an optimal feature subset. Then the confidence coefficient was proposed to determine the classification difficulty levels of samples. A combined classifier based on confidence coefficient was proposed for classification. Compared with the component classifiers and other individual classifiers, the combined classifier requires 34% less computing time and the mean classification accuracy rate is 0.2 to 2.23 percentage points higher for twelve subjects using leave-one-out cross validation. Experiment results confirm that the proposed method is effective to detect deception during network fraud simulation.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"44 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":"133238705","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: 0

Classification of Working Memory Performance from EEG with Deep Artificial Neural Networks 基于深度人工神经网络的脑电工作记忆分类

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737343

Youngchul Kwak, Woo‐Jin Song, Seong-Eun Kim

引用次数: 2

Mind Controlled Drone: An Innovative Multiclass SSVEP based Brain Computer Interface 精神控制无人机:一种创新的基于SSVEP的多级脑机接口

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737327

Andrei Chiuzbaian, J. Jakobsen, S. Puthusserypady

{"title":"Mind Controlled Drone: An Innovative Multiclass SSVEP based Brain Computer Interface","authors":"Andrei Chiuzbaian, J. Jakobsen, S. Puthusserypady","doi":"10.1109/IWW-BCI.2019.8737327","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737327","url":null,"abstract":"A crucial element lost in the context of a neurodegenerative disease is the possibility to freely explore and interact with the world around us. The work presented in this paper is focused on developing a brain-controlled Assistive Device (AD) to aid individuals in exploring the world around them with the help of a computer and their thoughts. By using the potential of a noninvasive Steady-State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system, the users can control a flying robot (also known as UAV or drone) in 3D physical space. From a video stream received from a video camera mounted on the drone, users can experience a degree of freedom while controlling the drone in 3D. The system proposed in this study uses a consumer-oriented headset, known as Emotiv Epoch in order to record the electroencephalogram (EEG) data. The system was tested on ten able-bodied subjects where four distinctive SSVEPs (5.3 Hz, 7 Hz, 9.4 Hz and 13.5 Hz) were detected and used as control signals for actuating the drone. A highly customizable visual interface was developed in order to elicit each SSVEP. The data recorded was filtered with an 8th order Butterworth bandpass filter and a fast Fourier transform (FFT) spectral analysis of the signal was applied in other to detect and classify each SSVEP. The proposed BCI system resulted in an average Information Transfer Rate (ITR) of 10 bits/min and a Positive Predictive Value (PPV) of 92.5%. The final conducted tests have demonstrated that the system proposed in this paper can easily control a drone in 3D space.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"179 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":"116840493","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: 22

Prediction of item familiarity based on ERPs 基于erp的项目熟悉度预测

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737330

Tanja Krumpe, W. Rosenstiel, M. Spüler

引用次数: 0

Novel Spatiospectral Features of ERPs Enhances Brain-Computer Interfaces 新的erp空间谱特征增强脑机接口

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737344

B. Abibullaev, Yerzhan Orazayev, A. Zollanvari

{"title":"Novel Spatiospectral Features of ERPs Enhances Brain-Computer Interfaces","authors":"B. Abibullaev, Yerzhan Orazayev, A. Zollanvari","doi":"10.1109/IWW-BCI.2019.8737344","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737344","url":null,"abstract":"Constructing accurate predictive models for the detection of event-related potentials (ERPs) is a crucial step to obtain robust Brain-Computer Interface (BCI) systems. In this regard, the majority of previous studies have used spatiotemporal features of ERPs for classification. Recently, we showed that the spatiospectral features of ERP signals also contain significant discriminatory effects in predicting users’ mental intent. In this study, we compare the discriminatory effect of spatiospectral features and spatiotemporal features of electroencephalographic signals. Spectral features are extracted by modeling ERP signals as a sum of sinusoids with unknown amplitudes, frequencies, and phases. Temporal features are the magnitude of ERP waveforms across time. As the classification rule Logistic Regression with L2-Ridge penalty (LRR) is used. We chose this classifier as we recently showed it could achieve high performance using spatiospectral features. We observe that generally by directly using temporal features rather than extracted spectral features even a higher classification performance is achieved.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"21 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":"126864349","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