Pub Date : 2017-12-08DOI: 10.1109/ICOT.2017.8336109
Lei Wang, R. Tong, C. Leung, S. Sivadas, Chongjia Ni, B. Ma
This paper provides an overall introduction of our Automatic Speech Recognition (ASR) systems for Southeast Asian languages. As not much existing work has been carried out on such languages, a few difficulties should be addressed before building the systems: limitation on speech and text resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia and Thai as examples to illustrate the strategies of collecting various resources required for building ASR systems.
{"title":"Cloud-based Automatic Speech Recognition systems for Southeast Asian Languages","authors":"Lei Wang, R. Tong, C. Leung, S. Sivadas, Chongjia Ni, B. Ma","doi":"10.1109/ICOT.2017.8336109","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336109","url":null,"abstract":"This paper provides an overall introduction of our Automatic Speech Recognition (ASR) systems for Southeast Asian languages. As not much existing work has been carried out on such languages, a few difficulties should be addressed before building the systems: limitation on speech and text resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia and Thai as examples to illustrate the strategies of collecting various resources required for building ASR systems.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"131 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132186644","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336075
Xuelian Cheng, Mingyi He, Weijun Duan
A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.
{"title":"Machine vision based physical fitness measurement with human posture recognition and skeletal data smoothing","authors":"Xuelian Cheng, Mingyi He, Weijun Duan","doi":"10.1109/ICOT.2017.8336075","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336075","url":null,"abstract":"A machine vision based measurement system for physical fitness is designed and implemented. Compared with other existing systems, our system only utilizes one Kinect sensor without bulky wearable sensors, thus enabling testees limber and free. To improve the test accuracy, a series of skeletal data smoothing methods and posture recognition algorithms are developed or used. The tests among university students and experimental results show that the performance of our system is increased and it is comparable with human beings, and therefore more practical and labor-saving.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124172057","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336111
Xiaoxi Ma, Lap-Pui Chau, Kim-Hui Yap
Recently, much research efforts have been dedicated to the development of computer-vision-based driver fatigue detection systems. Most of them utilize the RGB data, and focus on driver status detection during the day. However, drivers are more likely to be tired and drowsy during night time. In this paper, we present a driver fatigue detection system based on CNN using depth video sequences, which helps to provide alerts properly to fatigue drivers during the night time. Specifically, the two-stream CNN architecture incorporates spatial information of current depth frame and temporal information of neighboring depth frames which is represented by motion vectors. Besides, we propose a background removal system for depth video sequence of driving. Our method is trained and evaluated on our driver behavior dataset. Experiments show that the accuracy of the proposed method achieves 91.57%, which outperforms the baseline system within the recent state-of-the-art.
{"title":"Depth video-based two-stream convolutional neural networks for driver fatigue detection","authors":"Xiaoxi Ma, Lap-Pui Chau, Kim-Hui Yap","doi":"10.1109/ICOT.2017.8336111","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336111","url":null,"abstract":"Recently, much research efforts have been dedicated to the development of computer-vision-based driver fatigue detection systems. Most of them utilize the RGB data, and focus on driver status detection during the day. However, drivers are more likely to be tired and drowsy during night time. In this paper, we present a driver fatigue detection system based on CNN using depth video sequences, which helps to provide alerts properly to fatigue drivers during the night time. Specifically, the two-stream CNN architecture incorporates spatial information of current depth frame and temporal information of neighboring depth frames which is represented by motion vectors. Besides, we propose a background removal system for depth video sequence of driving. Our method is trained and evaluated on our driver behavior dataset. Experiments show that the accuracy of the proposed method achieves 91.57%, which outperforms the baseline system within the recent state-of-the-art.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126514078","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336093
Viet-Hang Duong, Manh-Quan Bui, P. Bao, Jia-Ching Wang
A new NMF model, spatial constrained graph sparse nonnegative matrix factorization (SGSNMF) is adopted for facial expression recognition. In this model, the extracted features preserve the topological structure of the original images and achieve sparseness from L2 constraint on coefficient matrix, meanwhile the base satisfy pixel dispersion penalty. The proposed method takes advantage of the project gradient decent and is based on the alternating nonnegative least square framework. Experiments on two facial expression recognition scenarios that involve a whole face and an occluded face reveal that the proposed algorithm outperforms the prevalent NMF methods.
{"title":"A new constrained nonnegative matrix factorization for facial expression recognition","authors":"Viet-Hang Duong, Manh-Quan Bui, P. Bao, Jia-Ching Wang","doi":"10.1109/ICOT.2017.8336093","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336093","url":null,"abstract":"A new NMF model, spatial constrained graph sparse nonnegative matrix factorization (SGSNMF) is adopted for facial expression recognition. In this model, the extracted features preserve the topological structure of the original images and achieve sparseness from L2 constraint on coefficient matrix, meanwhile the base satisfy pixel dispersion penalty. The proposed method takes advantage of the project gradient decent and is based on the alternating nonnegative least square framework. Experiments on two facial expression recognition scenarios that involve a whole face and an occluded face reveal that the proposed algorithm outperforms the prevalent NMF methods.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125479410","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336108
Wei Huang, Chuyu Wan, Peng Zhang, Yanning Zhang
Functional magnetic resonance images are widely known as an effective scanning tool in both clinical diagnosis and academic studies, to investigate soft tissues of specific regions in human beings, and it receives vast popularity because of its prominent merits in zero radiation, high spatial resolution and affordable scanning price. However, several critical issues still need to be carefully considered and tackled before acquired raw functional images are fed into the post-processing cycle, and the correction problem of partial volume effects is one of them. In this study, one special imaging modality of functional images, i.e., the arterial spin labeling, is emphasized, and its challenging correction problem of partial volume effects is to be solved. Significantly different from several contemporary correction studies in arterial spin labeling images, which mainly rely on neighboring pixels for adding additional information to facilitate the correction procedure, the new approach solely counts on the single pixel itself for its own correction problem. The superiority of the new approach can be suggested by its more clear corrected image outcomes without much blurring and significant tissues information loss, which are commonly witnessed in contemporary correction approaches for arterial spin labeling. Experiments based on a database composed of 360 demented patients and comprehensive analysis from the statistical perspective also substantiates that.
{"title":"A new correction approach of partial volume effects in functional MRI for orange computing","authors":"Wei Huang, Chuyu Wan, Peng Zhang, Yanning Zhang","doi":"10.1109/ICOT.2017.8336108","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336108","url":null,"abstract":"Functional magnetic resonance images are widely known as an effective scanning tool in both clinical diagnosis and academic studies, to investigate soft tissues of specific regions in human beings, and it receives vast popularity because of its prominent merits in zero radiation, high spatial resolution and affordable scanning price. However, several critical issues still need to be carefully considered and tackled before acquired raw functional images are fed into the post-processing cycle, and the correction problem of partial volume effects is one of them. In this study, one special imaging modality of functional images, i.e., the arterial spin labeling, is emphasized, and its challenging correction problem of partial volume effects is to be solved. Significantly different from several contemporary correction studies in arterial spin labeling images, which mainly rely on neighboring pixels for adding additional information to facilitate the correction procedure, the new approach solely counts on the single pixel itself for its own correction problem. The superiority of the new approach can be suggested by its more clear corrected image outcomes without much blurring and significant tissues information loss, which are commonly witnessed in contemporary correction approaches for arterial spin labeling. Experiments based on a database composed of 360 demented patients and comprehensive analysis from the statistical perspective also substantiates that.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"443 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125766631","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}
According to recent statistics reported by the United Nations, the world's elderly population continues to grow at an unprecedented rate. The global population of elderly people is projected to reach nearly the 2.1 billion by 2050. With the global trend towards an increasingly ageing population, tele-health solutions are required to provide efficient healthcare services for the elderly. The elderly are usually faced with many problems resulting from the deterioration of health with increasing age. One of the major problems in the elderly is falls — balance and gait disorders. Falls have significant effects on both physiological and psychological condition of elderly people. They consequently lead to fracture, serious injuries, disability or eventually death. To reduce falls and their consequences, in this paper, we propose a novel fall risk assessment system that can dynamically perform gait analysis in order to detect the risk of falls in the elderly in real-time. Our system utilizes a gait analyzing service as a stream component. It exploits acceleration data derived from a mobile device to remotely monitor gait parameters in a timely fashion. The preliminary experimental results demonstrate that our fall risk assessment system can be used to detect the risk of falls in real world settings and it is accurate enough to differentiate between the walking pattern of the elderly with normal gait and that of the elderly with abnormal gait.
{"title":"Real-time fall risk assessment system based on acceleration data","authors":"Watsawee Sansrimahachai, Manachai Toahchoodee, Rattanapol Piakaew, Teerapath Vijitphu, Supussara Jeenboonmee","doi":"10.1109/ICOT.2017.8336083","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336083","url":null,"abstract":"According to recent statistics reported by the United Nations, the world's elderly population continues to grow at an unprecedented rate. The global population of elderly people is projected to reach nearly the 2.1 billion by 2050. With the global trend towards an increasingly ageing population, tele-health solutions are required to provide efficient healthcare services for the elderly. The elderly are usually faced with many problems resulting from the deterioration of health with increasing age. One of the major problems in the elderly is falls — balance and gait disorders. Falls have significant effects on both physiological and psychological condition of elderly people. They consequently lead to fracture, serious injuries, disability or eventually death. To reduce falls and their consequences, in this paper, we propose a novel fall risk assessment system that can dynamically perform gait analysis in order to detect the risk of falls in the elderly in real-time. Our system utilizes a gait analyzing service as a stream component. It exploits acceleration data derived from a mobile device to remotely monitor gait parameters in a timely fashion. The preliminary experimental results demonstrate that our fall risk assessment system can be used to detect the risk of falls in real world settings and it is accurate enough to differentiate between the walking pattern of the elderly with normal gait and that of the elderly with abnormal gait.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125993624","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336105
Gema Syahidan Akbar, E. Kaburuan, V. Effendy
This study presents the interface design for Special Needs Children (SNC) autism of perception mid-high function which obtained from user persona and user needs. SNC tends to have no concept to manage time, it makes them difficult to understand what activities they need to do in their daily life, and therefore it is necessary to schedule events for SNC so that they know when and how the action should be done. The result is expected to help SNC and parents in learning to perform regular daily events and other activities provided by parents. In this research, User Interface also available for parents to input action and its step by step so SPC could understand that. The interface model can be used as a tool for SNC therapy to familiarize themselves in doing the activity at the right time. This research uses User-Centered Design (UCD) for designing the user interface with a focus on what the user needs and task. The result of this study shown that the interface model of scheduling activity increases the usability up to more than 85%.
{"title":"User interface (UI) design of scheduling activity apps for autistic children","authors":"Gema Syahidan Akbar, E. Kaburuan, V. Effendy","doi":"10.1109/ICOT.2017.8336105","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336105","url":null,"abstract":"This study presents the interface design for Special Needs Children (SNC) autism of perception mid-high function which obtained from user persona and user needs. SNC tends to have no concept to manage time, it makes them difficult to understand what activities they need to do in their daily life, and therefore it is necessary to schedule events for SNC so that they know when and how the action should be done. The result is expected to help SNC and parents in learning to perform regular daily events and other activities provided by parents. In this research, User Interface also available for parents to input action and its step by step so SPC could understand that. The interface model can be used as a tool for SNC therapy to familiarize themselves in doing the activity at the right time. This research uses User-Centered Design (UCD) for designing the user interface with a focus on what the user needs and task. The result of this study shown that the interface model of scheduling activity increases the usability up to more than 85%.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131275599","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336114
Zhuo Zhang, Haihong Zhang, Xinyang Li, Lu Zhang, Cuntai Guan
Recruiting and training sensory panelists for scent product research can be time consuming and costly. Along with the advent of EEG-based brain imaging technique, objective assessment of scent preference is of high interest in a variety of application domains. In this work we explore the EEG-based scent preference identification method. We first designed an effective and accurate data collection procedure. We proposed a machine learning algorithm, Spatial Temporal Subspace Optimization (STSO), for the discriminative subspace learning and classification modeling. A filter bank contains multiple band-pass filters is used to obtain EEG components from specific frequency ranges. Spatial subspace is constructed by exploring discriminative spatial components to enhance the spatial resolution of the EEG. Through the experiment, we confirm that brain signal can be identified in association with responses to pleasant and unpleasant odors, and there is a temporal pattern of such response because the temporal subspace optimization does improve the prediction result. However, event-related potentials were not present in our EEG data, and we have a discussion on the possible causes and implications. Our preliminary result shows that scent can be identified with moderate accuracy.
{"title":"Toward EEG-based Olfactory Sensing through Spatial Temporal Subspace Optimization","authors":"Zhuo Zhang, Haihong Zhang, Xinyang Li, Lu Zhang, Cuntai Guan","doi":"10.1109/ICOT.2017.8336114","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336114","url":null,"abstract":"Recruiting and training sensory panelists for scent product research can be time consuming and costly. Along with the advent of EEG-based brain imaging technique, objective assessment of scent preference is of high interest in a variety of application domains. In this work we explore the EEG-based scent preference identification method. We first designed an effective and accurate data collection procedure. We proposed a machine learning algorithm, Spatial Temporal Subspace Optimization (STSO), for the discriminative subspace learning and classification modeling. A filter bank contains multiple band-pass filters is used to obtain EEG components from specific frequency ranges. Spatial subspace is constructed by exploring discriminative spatial components to enhance the spatial resolution of the EEG. Through the experiment, we confirm that brain signal can be identified in association with responses to pleasant and unpleasant odors, and there is a temporal pattern of such response because the temporal subspace optimization does improve the prediction result. However, event-related potentials were not present in our EEG data, and we have a discussion on the possible causes and implications. Our preliminary result shows that scent can be identified with moderate accuracy.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117143618","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336078
Xiaoyong Lu, Aibao Zhou, Hongwu Yang
Clinical depression can be characterized by a range of psychological factors, resulting in social, occupational and educational impaired function. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. Such methods are subjective and single in nature, and lack an objective predictor of depression. This project aims at developing a novel method for diagnosis of depression using speech analysis from psychological perspective. It is well known that the Self is not only the cognitive subject, but also the core of personality. In this PhD work, for above reason, classical scientific psychology paradigms are employed on abnormalities of self-related processing in patients from different dimensions of the Self, and speech signal processing methods and Machine Learning methods are adopted for depressed speech. We believe the method can better capture psychological characteristics of depressed patients, and make a meaningful progress in improving diagnosis accuracy.
{"title":"A novel method design for diagnosis of psychological symptoms of depression using speech analysis","authors":"Xiaoyong Lu, Aibao Zhou, Hongwu Yang","doi":"10.1109/ICOT.2017.8336078","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336078","url":null,"abstract":"Clinical depression can be characterized by a range of psychological factors, resulting in social, occupational and educational impaired function. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. Such methods are subjective and single in nature, and lack an objective predictor of depression. This project aims at developing a novel method for diagnosis of depression using speech analysis from psychological perspective. It is well known that the Self is not only the cognitive subject, but also the core of personality. In this PhD work, for above reason, classical scientific psychology paradigms are employed on abnormalities of self-related processing in patients from different dimensions of the Self, and speech signal processing methods and Machine Learning methods are adopted for depressed speech. We believe the method can better capture psychological characteristics of depressed patients, and make a meaningful progress in improving diagnosis accuracy.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116381629","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 : 2017-12-01DOI: 10.1109/ICOT.2017.8336116
S. Ehrlich, Cuntai Guan, G. Cheng
Research on human emotions and underlying brain processes is mostly performed open-loop, e.g. by presenting emotional stimuli and measuring subject's brain responses. Investigating human emotions in interaction with emotional stimuli (closed-loop) significantly complicates experimental setups and has so far rarely been proposed. We present concept and technical realization of an electroencephalography (EEG)-based affective Brain-Computer Interface (BCI) to study emotional brain processes in continuous closed-loop interaction. Our BCI consists of an algorithm generating continuous patterns of synthesized affective music, embedded in an online BCI architecture. An initial calibration is employed to obtain user-specific models associating EEG patterns with affective content in musical patterns. These models are then used in online application to translate the user's affect into a continuous musical representation; playback to the user results in closed-loop affective brain-interactions. The proposed BCI provides a platform to stimulate the brain in a closed-loop fashion, offering novel approaches to study human sensorimotor integration and emotions.
{"title":"A closed-loop brain-computer music interface for continuous affective interaction","authors":"S. Ehrlich, Cuntai Guan, G. Cheng","doi":"10.1109/ICOT.2017.8336116","DOIUrl":"https://doi.org/10.1109/ICOT.2017.8336116","url":null,"abstract":"Research on human emotions and underlying brain processes is mostly performed open-loop, e.g. by presenting emotional stimuli and measuring subject's brain responses. Investigating human emotions in interaction with emotional stimuli (closed-loop) significantly complicates experimental setups and has so far rarely been proposed. We present concept and technical realization of an electroencephalography (EEG)-based affective Brain-Computer Interface (BCI) to study emotional brain processes in continuous closed-loop interaction. Our BCI consists of an algorithm generating continuous patterns of synthesized affective music, embedded in an online BCI architecture. An initial calibration is employed to obtain user-specific models associating EEG patterns with affective content in musical patterns. These models are then used in online application to translate the user's affect into a continuous musical representation; playback to the user results in closed-loop affective brain-interactions. The proposed BCI provides a platform to stimulate the brain in a closed-loop fashion, offering novel approaches to study human sensorimotor integration and emotions.","PeriodicalId":297245,"journal":{"name":"2017 International Conference on Orange Technologies (ICOT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126927350","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}
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