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

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Protection of EEG Data using Blockchain Platform 基于区块链平台的EEG数据保护
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737260
Sujin Bak, Yeon Pyo, Jichai Jeong
{"title":"Protection of EEG Data using Blockchain Platform","authors":"Sujin Bak, Yeon Pyo, Jichai Jeong","doi":"10.1109/IWW-BCI.2019.8737260","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737260","url":null,"abstract":"Brain-computer interface can be currently accelerated to develop the exoskeleton for healthy people as well as patients who are unable to move muscles around the world. In this situation, the communication between the electroencephalogram (EEG) and prosthesis discovers the vulnerabilities to taking personal information. However, previous researches only focus on the analysis of attack pattern rather than fixing the vulnerability. In order to complement the vulnerability, we propose a blockchain platform in which try to identify the modulated data when server is attacked. Also, we find out potential risks in EEG data with non-blockchain environments after attack in our study. As a result, the proposed system can guarantee the integrity of EEG data by knowing the change of hash, and can prevent attacks such as hijacking, sniffing, and eavesdropping.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"36 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":"123722438","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: 8
Neural response to grasp of robot hand from M1 area of Rhesus monkey 恒河猴M1区对机械手抓取的神经反应
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737255
Seong-Min Kim, Sung-Yong Hyun, Jeong-woo Sohn, Soyong Chae, Sung-Phil Kim
{"title":"Neural response to grasp of robot hand from M1 area of Rhesus monkey","authors":"Seong-Min Kim, Sung-Yong Hyun, Jeong-woo Sohn, Soyong Chae, Sung-Phil Kim","doi":"10.1109/IWW-BCI.2019.8737255","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737255","url":null,"abstract":"A brain machine interface (BMI) is a technology that makes direct connections between neural systems and external device. BMI is often used in platform to restore function for patients with paralysis. In preclinical trials, non-human primate is a preferable choice for animal model especially for upper-limb neuroprosthetics. However, there is gap in the decoder from the animal model to apply the result to human. This is because the animal can use their upper limb in normal circumstances but patients cannot. To overcome this gap, we set up task in which a monkey was required only to observe reaching to grasp motion of robot arm and hand while we were collecting single unit activities from the primary motor cortex (M1). We analyzed the neuronal activities focusing on period of early stage, late stage of movement of robot arm, and grasping of robot hand respectively. There were neuronal groups showing increased activities for each time periods. Some of neurons showed increased activities during late stage of movement and grasping. This result has shown that neural decoder based on observation could be used for reaching to grasp type brain-machine interface in human especially for grasping.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"8 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":"123740323","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: 2
Interference in tactile discrmination performance by neuronal modulation 神经元调制对触觉辨别性能的干扰
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737347
Gaeun Jeong, J. Kim, Seokyun Ryun, C. Chung
{"title":"Interference in tactile discrmination performance by neuronal modulation","authors":"Gaeun Jeong, J. Kim, Seokyun Ryun, C. Chung","doi":"10.1109/IWW-BCI.2019.8737347","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737347","url":null,"abstract":"Perceiving and processing sensory stimuli are essential to generate motor action. Previous studies suggested features of vibrotactile stimulus such as amplitude and frequency are differently represented onto somatosensory cortices so that the stimulus can be discriminated. In the present study, we aimed to demonstrate the effect of transcranial magnetic stimulation (TMS) triplet pulses over primary somatosensory cortex (S1) or secondary somatosensory cortex (S2) on a tactile discrimination task. In two alternative forced choice task, TMS over S1 or S2 significantly interfered with the discrimination performance. This disruptive influence was mostly shown when the vibrotactile stimulus was close to high frequency (320Hz). Therefore we concluded the inhibitory effect of TMS is selective with tactile frequency.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"39 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":"124369943","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
The Effect of a Binaural Beat Combined with Autonomous Sensory Meridian Response Triggers on Brainwave Entrainment 双耳节拍结合自主感觉经络反应触发对脑电波夹带的影响
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737329
C. Song, No-Sang Kwak, Minji Lee, Seong-Whan Lee
{"title":"The Effect of a Binaural Beat Combined with Autonomous Sensory Meridian Response Triggers on Brainwave Entrainment","authors":"C. Song, No-Sang Kwak, Minji Lee, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2019.8737329","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737329","url":null,"abstract":"Brainwave entrainment means matching the frequency of the brainwave to that of external rhythmic stimuli. Auditory stimuli have been mostly used in brainwave entrainment. In this study, we aim to induce a 6 Hz brainwave in accord with the theta wave that activates in non-rapid eye movement sleep stage 1. Here, we use a novel auditory stimulus combined with a binaural beat (BB) and an autonomous sensory meridian response (ASMR) trigger (AT) because AT could give psychological stability to users while BB induces activation of target brain signals for desired mental states (e.g. meditation or sleep). In our experiment, we investigate an effect of the combined auditory stimuli with the three different decibel ratios. As a result, we confirm that the proposed stimuli could induce both effects of BB and AT, simultaneously. Also, statistical analysis on questionnaires shows that changes of emotional states before and after stimulation are statistically significant.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"100 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":"114237844","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: 2
Domain Adaptation with Source Selection for Motor-Imagery based BCI 基于运动图像的脑机接口域自适应与源选择
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737340
Eunjin Jeon, Wonjun Ko, Heung-Il Suk
{"title":"Domain Adaptation with Source Selection for Motor-Imagery based BCI","authors":"Eunjin Jeon, Wonjun Ko, Heung-Il Suk","doi":"10.1109/IWW-BCI.2019.8737340","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737340","url":null,"abstract":"Recent successes of deep learning methods in various applications have inspired BCI researchers for their use in EEG classification. However, data insufficiency and high intra- and inter-subject variabilities hinder from taking their advantage of discovering complex patterns inherent in data, which can be potentially useful to enhance EEG classification accuracy. In this paper, we devise a novel framework of training a deep network by adapting samples of other subjects as a means of domain adaptation. Assuming that there are EEG trials of motor-imagery tasks from multiple subjects available, we first select a subject whose EEG signal characteristics are similar to the target subject based on their power spectral density in resting-state EEG signals. We then use EEG signals of both the selected subject (called a source subject) and the target subject jointly in training a deep network. Rather than training a single path network, we adopt a multi-path network architecture, where the shared bottom layers are used to discover common features for both source and target subjects, while the upper layers branch out into (1) source-target subject identification, (2) label prediction optimized for a source subject, and (3) label prediction optimized for a target subject. Based on our experimental results over the BCI Competition IV-IIa dataset, we validated the effectiveness of the proposed framework in various aspects.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"33 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":"122410188","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: 23
Classification of Functional Near-Infrared Spectrocopy Signals during Passive and Combinatory Exercises for Neurorehabilitation 神经康复被动和组合训练中功能性近红外光谱信号的分类
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737332
Changhee Han, Jinuk Kwon, Han-Jeong Hwang, C. Im
{"title":"Classification of Functional Near-Infrared Spectrocopy Signals during Passive and Combinatory Exercises for Neurorehabilitation","authors":"Changhee Han, Jinuk Kwon, Han-Jeong Hwang, C. Im","doi":"10.1109/IWW-BCI.2019.8737332","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737332","url":null,"abstract":"In this study, we evaluated and classified hemodynamic responses induced by conventional passive exercise for neurorehabilitation and combined exercise strategy (passive exercise with active motor execution or motor imagery). Functional near infrared spectroscopy (fNIRS) was recorded while eight healthy subjects conducted three different tasks (passive motor execution alone, passive motor execution with motor imagery, and passive motor execution with active motor execution). From the results, stronger and broader activation around the sensorimotor cortex was observed when subjects performed the combinatory exercises. Results of pattern classification showed classification accuracy higher than 80 %, demonstrating that fNIRS could be used as a potential tool to assess users’ cognitive engagement in the combinatory neurorehabilitation strategy.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"16 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121004625","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
Real-time Decoding of EEG Gait Intention for Controlling a Lower-limb Exoskeleton System 用于控制下肢外骨骼系统的EEG步态意向实时解码
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737311
Junhyuk Choi, Hyungmin Kim
{"title":"Real-time Decoding of EEG Gait Intention for Controlling a Lower-limb Exoskeleton System","authors":"Junhyuk Choi, Hyungmin Kim","doi":"10.1109/IWW-BCI.2019.8737311","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737311","url":null,"abstract":"In this study, we demonstrate real-time gait intention recognition algorithm which can decode voluntary gait execution from electroencephalography (EEG) for controlling the lower-limb exoskeleton. EEG gait intention features were measured by Mu-band Event-Related Desynchronization (ERD) and classified. The Receiver Operating Characteristic (ROC) curve was used for clarifying the classification performance corresponding to the length of training data. We also proposed a modified threshold method for time series binary classification to minimize the false detection rate.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"11 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":"124622592","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: 12
An online top-down SSVEP-BMI for augmented reality 一个在线自上而下的增强现实SSVEP-BMI
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737348
Ji-Wan Kim, Maeng-Nam Kim, Dong-Hyeon Kang, Min-Hee Ahn, Hyun Seok Kim, Byoung-Kyong Min
{"title":"An online top-down SSVEP-BMI for augmented reality","authors":"Ji-Wan Kim, Maeng-Nam Kim, Dong-Hyeon Kang, Min-Hee Ahn, Hyun Seok Kim, Byoung-Kyong Min","doi":"10.1109/IWW-BCI.2019.8737348","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737348","url":null,"abstract":"Augmented reality (AR) technology using a head mounted display (HMD) is one of the fundamental tools in the next smart internet of things (IoT) society. Nowadays, portable brain-machine interfaces (BMIs) using an HMD have been studied for the future of BMI interlocked with the present IoT technology. In order to investigate the feasibility of the top-down SSVEP (steady-state visual evoked potential) BMI embedded in an HMD, SSVEP stimuli was presented in a HoloLens (Microsoft) for augmented reality (AR) constructed by holography. Electroencephalogram (EEG) was measured during the top-down SSVEP-based BMI performance, where a grid-shaped flickering visual stimulus was presented in the display of HoloLens. We examined its feasibility in a real-time basis by its decoding accuracy. We found that the top-down SSVEP-BMI could be efficiently embedded in an AR-based HMD, and thus it can be applied for the AR-based device-control automation in an IoT space using EEG signals.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"85 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":"127536767","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: 5
Steady-State Somatosensory Evoked Potential based Brain-Computer Interface for Sit-to-Stand Movement Intention 基于稳态体感诱发电位的坐立运动意向脑机接口
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737335
Kabmun Cha, Jaehyung Lee, Hyungmin Kim, Choong Hyun Kim, S. Lee
{"title":"Steady-State Somatosensory Evoked Potential based Brain-Computer Interface for Sit-to-Stand Movement Intention","authors":"Kabmun Cha, Jaehyung Lee, Hyungmin Kim, Choong Hyun Kim, S. Lee","doi":"10.1109/IWW-BCI.2019.8737335","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737335","url":null,"abstract":"The purpose of this study was to develop sit-to-stand movement intention decoding algorithms based on brain responses to the vibrotactile stimulation. Specifically, we studied a simultaneous hybrid brain-computer interface (BCI) by combining steady-state somatosensory evoked potential (SSSEP) and a motor imagery (MI) task. In our BCI system, a user could generate two possible commands by concentrating on one of two vibration stimuli, which were attached to the left and right hand. The statistical method based on the mutual information between the spatial-temporal patterns was used to detect the user’s intention of sit or stand from the electroencephalography (EEG) signals. The results of our offline experiments demonstrated the feasibility of hybrid MI-SSSEP based BCI system for decoding sit-to-stand movement intention. It is expected that the proposed method and algorithms can be implanted in the future to control the lower limb exoskeleton robot.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"23 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":"126614489","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: 3
Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI 基于脑电的脑机接口中协方差矩阵和交叉组合区域的最优信道选择
2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI: 10.1109/IWW-BCI.2019.8737257
Yongkoo Park, Wonzoo Chung
{"title":"Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI","authors":"Yongkoo Park, Wonzoo Chung","doi":"10.1109/IWW-BCI.2019.8737257","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737257","url":null,"abstract":"The EEG-based brain-computer interface (BCI) requires removal of irrelevant channels to improve performance. In this paper, we propose the optimal channel selection using EEG channel covariance matrix and cross-combining region. First, the discriminative H channels and target channel are selected by difference of EEG channel covariance matrix between two classes. Second, we configure several sub-channel regions to cover the H channels. Then, we extract FBCSP features from cross-combining regions which are combination of the sub-channel regions and target channel. We select the best one cross-combining region and the optimal channels which are included in selected cross-combining region are finally selected. The features of selected region are used as input of LS-SVM classifier. The simulation results show the performance improvement of proposed method for BCI competition III dataset IVa by comparing the conventional channel selection methods.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"35 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":"129569708","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: 8
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