Pub Date : 1900-01-01DOI: 10.1109/IWW-BCI.2017.7858167
Seung-Bo Lee, Eun-Jin Jeong, Yunsik Son, Dong-Joo Kim
Computed tomography (CT) is useful to investigate the presence and severity of injury during acute stage of traumatic brain injury (TBI) due to its availability and short image acquisition time. Recently, quantitative CT analysis have shown promising results in objective and accurate assessment of lesion and the prediction of outcome. To conduct further multicenter, longitudinal follow-up studies using quantitative analysis, the effect of CT scanner manufacturer should be investigated. In this study, CT images were acquired from 326 subjects without any apparent intracranial abnormalities. The images were scanned by three different scanner manufacturers. The quantitative analysis was performed and plotted as density distribution. The acquired density distributions were served as input features of support vector machine (SVM) using Gaussian kernel function, which is designed for classifying the CT images based on the scanner manufacturers. The optimal hyperparameters were explored via grid search test and the model increased the robustness by 5-fold cross validation. The best predictive performance was obtained when C = 100 and γ = 0.1 (accuracy = 91.1 %). The results showed significant difference in density distribution according to the scanner manufacturers, and thus suggest that the manufacturer should be standardized to conduct the quantitative analysis on the brain CT images.
{"title":"Classification of computed tomography scanner manufacturer using support vector machine","authors":"Seung-Bo Lee, Eun-Jin Jeong, Yunsik Son, Dong-Joo Kim","doi":"10.1109/IWW-BCI.2017.7858167","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858167","url":null,"abstract":"Computed tomography (CT) is useful to investigate the presence and severity of injury during acute stage of traumatic brain injury (TBI) due to its availability and short image acquisition time. Recently, quantitative CT analysis have shown promising results in objective and accurate assessment of lesion and the prediction of outcome. To conduct further multicenter, longitudinal follow-up studies using quantitative analysis, the effect of CT scanner manufacturer should be investigated. In this study, CT images were acquired from 326 subjects without any apparent intracranial abnormalities. The images were scanned by three different scanner manufacturers. The quantitative analysis was performed and plotted as density distribution. The acquired density distributions were served as input features of support vector machine (SVM) using Gaussian kernel function, which is designed for classifying the CT images based on the scanner manufacturers. The optimal hyperparameters were explored via grid search test and the model increased the robustness by 5-fold cross validation. The best predictive performance was obtained when C = 100 and γ = 0.1 (accuracy = 91.1 %). The results showed significant difference in density distribution according to the scanner manufacturers, and thus suggest that the manufacturer should be standardized to conduct the quantitative analysis on the brain CT images.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124983444","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}
Pub Date : 1900-01-01DOI: 10.1109/IWW-BCI.2017.7858162
Meina Zhao, Lingdi Liu, Jing Wang, Gang Zhao
The change in human brain signals and their event-related potential (ERP) components are observed as a reflection of consumers' emotions when examining a buying process. The product-service system (PSS) is a comprehensive business model that is able to fulfill user requirements by providing a mix of products and services. Services can improve the competitiveness of products and enhance customer satisfaction, but there is lack of research on the root of service competitiveness. In this experiment, participants were shown products and related services that were available for purchase. The emotional ERP component, the EPN, was elicited by the service conditions and distributed over left frontal regions, which was different with physical products stimulus. The main findings of the experiment confirm that the positive emotional connotations are processed in the left frontal region. This result helps us better understand the positive emotions are stimulated during the services decision making process, and in order to better understand the different perception of physical products and service. Based on the emotional motivation of the consumer, the EPN may be emotional indicators for measuring consumers' evaluations of service, providing a neural view of PSS buying decisions.
{"title":"The neural analysis of “why service can improve product competitiveness”","authors":"Meina Zhao, Lingdi Liu, Jing Wang, Gang Zhao","doi":"10.1109/IWW-BCI.2017.7858162","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858162","url":null,"abstract":"The change in human brain signals and their event-related potential (ERP) components are observed as a reflection of consumers' emotions when examining a buying process. The product-service system (PSS) is a comprehensive business model that is able to fulfill user requirements by providing a mix of products and services. Services can improve the competitiveness of products and enhance customer satisfaction, but there is lack of research on the root of service competitiveness. In this experiment, participants were shown products and related services that were available for purchase. The emotional ERP component, the EPN, was elicited by the service conditions and distributed over left frontal regions, which was different with physical products stimulus. The main findings of the experiment confirm that the positive emotional connotations are processed in the left frontal region. This result helps us better understand the positive emotions are stimulated during the services decision making process, and in order to better understand the different perception of physical products and service. Based on the emotional motivation of the consumer, the EPN may be emotional indicators for measuring consumers' evaluations of service, providing a neural view of PSS buying decisions.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115134379","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}
Pub Date : 1900-01-01DOI: 10.1109/IWW-BCI.2017.7858155
Youngjae Song, F. Sepulveda
This research investigated an online onset detection (i.e., ON state detection in asynchronous BCIs) method for BCIs by opening a message when it arrives in two different daily-life task scenarios (watching video and reading text). A new sound-production related cognitive task (Sound-production imagery, SI) was tested. Blind-source separation with canonical correlation analysis was used for artefact handling. Autoregressive coefficients, band power, common spatial patterns and discrete wavelet transform were used for feature extraction to cover all time, frequency, and spatial time-frequency domain. Linear discriminant analysis was used for classification. The averaged true-positive rate with six subjects was 88.9% in the watching video scenario and 78.9% in the reading text case. The average false-positive rates were 4.2% and 3.9%, respectively. In terms of task response speed, SI task recognition took 4.45s on average for an onset. From these results, the new SI task showed promising results for an online self-paced onset detection system compared to other similar studies.
{"title":"An online self-paced brain-computer interface onset detection based on sound-production imagery applied to real-life scenarios","authors":"Youngjae Song, F. Sepulveda","doi":"10.1109/IWW-BCI.2017.7858155","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858155","url":null,"abstract":"This research investigated an online onset detection (i.e., ON state detection in asynchronous BCIs) method for BCIs by opening a message when it arrives in two different daily-life task scenarios (watching video and reading text). A new sound-production related cognitive task (Sound-production imagery, SI) was tested. Blind-source separation with canonical correlation analysis was used for artefact handling. Autoregressive coefficients, band power, common spatial patterns and discrete wavelet transform were used for feature extraction to cover all time, frequency, and spatial time-frequency domain. Linear discriminant analysis was used for classification. The averaged true-positive rate with six subjects was 88.9% in the watching video scenario and 78.9% in the reading text case. The average false-positive rates were 4.2% and 3.9%, respectively. In terms of task response speed, SI task recognition took 4.45s on average for an onset. From these results, the new SI task showed promising results for an online self-paced onset detection system compared to other similar studies.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115232999","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}
Pub Date : 1900-01-01DOI: 10.1109/IWW-BCI.2017.7858153
H. Yeom, J. Kim, C. Chung
Over the last several decades, there have been lots of BMI studies. However, it is still difficult to use BMI system in real life. Here, we introduce our three BMI studies to overcome these problems. First, we predicted continuous movement trajectory from non-invasive MEG signals. Second, we proposed a new BMI prediction model to increase the prediction accuracy using external stereo camera. Finally, we showed that modes of the BMI system can be changed according to the user's brain state. Based on our results, we expect that practical and high accuracy BMI system will be possible by combining brain states and feedback information.
{"title":"Practical brain-machine interface system","authors":"H. Yeom, J. Kim, C. Chung","doi":"10.1109/IWW-BCI.2017.7858153","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858153","url":null,"abstract":"Over the last several decades, there have been lots of BMI studies. However, it is still difficult to use BMI system in real life. Here, we introduce our three BMI studies to overcome these problems. First, we predicted continuous movement trajectory from non-invasive MEG signals. Second, we proposed a new BMI prediction model to increase the prediction accuracy using external stereo camera. Finally, we showed that modes of the BMI system can be changed according to the user's brain state. Based on our results, we expect that practical and high accuracy BMI system will be possible by combining brain states and feedback information.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130374218","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}
Pub Date : 1900-01-01DOI: 10.1109/IWW-BCI.2017.7858163
Gihyoun Lee, Seung Hyun Lee, S. Jin, J. An
The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (fNIRS) imaging analysis as well. The GLM has drawback of failure in fNIRS signals, when they have drift globally. Wavelet based de-trending technique is very popular to correct the baseline drift (BD) in fNIRS. However, this method globally distorted the total multichannel signals even if just one channel's signal was locally drifted. This paper suggests BD detection index to indicate BD as an objective index. The experiments show the performance of the proposed detection index as graphic results with current de-trending algorithm.
{"title":"Baseline drift detection index using wavelet transform analysis for fNIRS signal","authors":"Gihyoun Lee, Seung Hyun Lee, S. Jin, J. An","doi":"10.1109/IWW-BCI.2017.7858163","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858163","url":null,"abstract":"The general linear model (GLM) as a standard model for fMRI analysis has been applied to functional near-infrared spectroscopic (fNIRS) imaging analysis as well. The GLM has drawback of failure in fNIRS signals, when they have drift globally. Wavelet based de-trending technique is very popular to correct the baseline drift (BD) in fNIRS. However, this method globally distorted the total multichannel signals even if just one channel's signal was locally drifted. This paper suggests BD detection index to indicate BD as an objective index. The experiments show the performance of the proposed detection index as graphic results with current de-trending algorithm.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131556115","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：