Pub Date : 2017-02-16DOI: 10.1109/IWW-BCI.2017.7858159
Hoseok Choi, D. Jang, K. Lee
In arm movement BCI (brain-computer interface), the unimanual research has been well. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: the movement conditions classification, and 2nd step: the hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.
{"title":"Bimanual Arm Movements Decoding using Hybrid Method","authors":"Hoseok Choi, D. Jang, K. Lee","doi":"10.1109/IWW-BCI.2017.7858159","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858159","url":null,"abstract":"In arm movement BCI (brain-computer interface), the unimanual research has been well. However, the bimanual brain state is known to be different from the unimanual one, so the conventional arm movement decoding method seems to be insufficient to decode bimanual movement. In this research, we suggested the hybrid method to improve the decoding accuracy for bimanual movement estimation. The method consists of two step; 1st step: the movement conditions classification, and 2nd step: the hand trajectory prediction algorithm. As a result, the hybrid method showed improved arm movement decoding performance and significant and stable decoding rate over several months for bimanual tasks. This technique could be applied to arm movement BCI in real world and the various neuro-prosthetics fields.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897538","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 : 2017-02-16DOI: 10.1109/IWW-BCI.2017.7858142
J. Contreras-Vidal, Jesus G. Cruz-Garza, Anastasiya E. Kopteva
The restoration and rehabilitation of human bipedal locomotion represent major goals for brain machine interfaces (BMIs), i.e., devices that translate neural activity into motor commands to control wearable robots to enable locomotive and non-locomotive tasks by individuals with gait disabilities. Prior BMI efforts based on scalp electroencephalography (EEG) have revealed that fluctuations in the amplitude of slow cortical potentials in the delta band contain information that can be used to infer motor intent, and more specifically, the kinematics of walking and non-locomotive tasks such as sitting and standing. However, little is known about the extent to which EEG can be used to discern the expressive qualities that influence such functional movements. Here, we discuss how novel experimental approaches integrated with machine learning techniques can deployed to decode expressive qualities of movement. Applications to artistic brain-computer interfaces (BCIs), movement aesthetics, and gait neuroprostheses endowed with expressive qualities are discussed.
{"title":"Towards a whole body brain-machine interface system for decoding expressive movement intent Challenges and Opportunities","authors":"J. Contreras-Vidal, Jesus G. Cruz-Garza, Anastasiya E. Kopteva","doi":"10.1109/IWW-BCI.2017.7858142","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858142","url":null,"abstract":"The restoration and rehabilitation of human bipedal locomotion represent major goals for brain machine interfaces (BMIs), i.e., devices that translate neural activity into motor commands to control wearable robots to enable locomotive and non-locomotive tasks by individuals with gait disabilities. Prior BMI efforts based on scalp electroencephalography (EEG) have revealed that fluctuations in the amplitude of slow cortical potentials in the delta band contain information that can be used to infer motor intent, and more specifically, the kinematics of walking and non-locomotive tasks such as sitting and standing. However, little is known about the extent to which EEG can be used to discern the expressive qualities that influence such functional movements. Here, we discuss how novel experimental approaches integrated with machine learning techniques can deployed to decode expressive qualities of movement. Applications to artistic brain-computer interfaces (BCIs), movement aesthetics, and gait neuroprostheses endowed with expressive qualities are discussed.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124053911","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 : 2017-02-16DOI: 10.1109/IWW-BCI.2017.7858156
Ji-Hoon Jeong, Min-Ho Lee, No-Sang Kwak, Seong-Whan Lee
Bran-machine interface (BMI) can be used for controlling of external devices such as the exoskeleton, robot arm, etc. For efficient communication between a user and machine, fast and accurate detection of user intention is important elements in the BMI application. For this reason, readiness potential (RP) is a useful feature that is possible to detect movement intention before the movement onset. To our knowledge, however, the analysis of single-trial RP component has not been sufficiently investigated in the real-world application (e.g. powered exoskeleton or robot arm). In our study, we first validate a single-trial RP performance in the lower limb exoskeleton environment where the user allows for voluntary walking. The experiments are executed in the two different walking conditions which are normal and exoskeleton walking. The Laplacian and common average reference (CAR) filters are applied to reduce spatial noise and regularized linear discriminant analysis (RLDA) is used as a classifier. Our results show the averaged classification accuracy of 80.7% for 5 subjects. This study demonstrates a feasibility of RP-based BMI system for controlling of a lower limb exoskeleton.
{"title":"Single-trial analysis of readiness potentials for lower limb exoskeleton control","authors":"Ji-Hoon Jeong, Min-Ho Lee, No-Sang Kwak, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2017.7858156","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858156","url":null,"abstract":"Bran-machine interface (BMI) can be used for controlling of external devices such as the exoskeleton, robot arm, etc. For efficient communication between a user and machine, fast and accurate detection of user intention is important elements in the BMI application. For this reason, readiness potential (RP) is a useful feature that is possible to detect movement intention before the movement onset. To our knowledge, however, the analysis of single-trial RP component has not been sufficiently investigated in the real-world application (e.g. powered exoskeleton or robot arm). In our study, we first validate a single-trial RP performance in the lower limb exoskeleton environment where the user allows for voluntary walking. The experiments are executed in the two different walking conditions which are normal and exoskeleton walking. The Laplacian and common average reference (CAR) filters are applied to reduce spatial noise and regularized linear discriminant analysis (RLDA) is used as a classifier. Our results show the averaged classification accuracy of 80.7% for 5 subjects. This study demonstrates a feasibility of RP-based BMI system for controlling of a lower limb exoskeleton.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115548052","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 : 2017-02-16DOI: 10.1109/IWW-BCI.2017.7858164
J. Hwang, Min-Ho Lee, Seong-Whan Lee
In brain-computer interface (BCI) research, spellers are valuable issues because they can provide communication channel to human. In this paper, we propose a novel hybrid speller that is SSVEP feedback with peripheral-vision stimulus to the conventional P300 paradigm. A canonical correlation analysis (CCA) and a linear discriminant analysis (LDA) classified SSVEP and P300, respectively. Four subjects participated in experiments, in which accuracy was compared with those of other spellers. Proposed approach revealed sufficient P300 and SSVEP potentials without interaction effect and time consuming, and also reduced visual fatigue. The results show that this research suggests a promising approach to make the speller more time-efficient.
{"title":"A brain-computer interface speller using peripheral stimulus-based SSVEP and P300","authors":"J. Hwang, Min-Ho Lee, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2017.7858164","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858164","url":null,"abstract":"In brain-computer interface (BCI) research, spellers are valuable issues because they can provide communication channel to human. In this paper, we propose a novel hybrid speller that is SSVEP feedback with peripheral-vision stimulus to the conventional P300 paradigm. A canonical correlation analysis (CCA) and a linear discriminant analysis (LDA) classified SSVEP and P300, respectively. Four subjects participated in experiments, in which accuracy was compared with those of other spellers. Proposed approach revealed sufficient P300 and SSVEP potentials without interaction effect and time consuming, and also reduced visual fatigue. The results show that this research suggests a promising approach to make the speller more time-efficient.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128932845","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 : 2017-02-16DOI: 10.1109/IWW-BCI.2017.7858177
Damir Nurseitov, Abzal Serekov, A. Shintemirov, B. Abibullaev
With the development of Brain-Computer Interface (BCI) systems people with motor disabilities are able to control external devices using their thoughts. To control a device through BCI, brain activities of the user must be accurately translated to meaningful commands and a design of appropiate BCI paradigms play important roles in such tasks. This work presents a design and evaluation of a BCI system that is based on P300 Event-Related Potentials (ERP) in order to control a mobile robot platform into four directions (left, right, front, back). The ultimate goal of this research is to provide convienient way of controlling a mobile robot as an assistive home technology for disabled people. Low cost EPOC Emotiv headset was used in the BCI system to acquire brain signals with a Jaguar 4x4 Wheel robot as a control platform. We discuss a set of signal processing steps employed in detail and the utility of a regularized logistic regression classifier to detect visual stimuli induced P300 ERPs and, to control the Jaguar robot.
{"title":"Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot","authors":"Damir Nurseitov, Abzal Serekov, A. Shintemirov, B. Abibullaev","doi":"10.1109/IWW-BCI.2017.7858177","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858177","url":null,"abstract":"With the development of Brain-Computer Interface (BCI) systems people with motor disabilities are able to control external devices using their thoughts. To control a device through BCI, brain activities of the user must be accurately translated to meaningful commands and a design of appropiate BCI paradigms play important roles in such tasks. This work presents a design and evaluation of a BCI system that is based on P300 Event-Related Potentials (ERP) in order to control a mobile robot platform into four directions (left, right, front, back). The ultimate goal of this research is to provide convienient way of controlling a mobile robot as an assistive home technology for disabled people. Low cost EPOC Emotiv headset was used in the BCI system to acquire brain signals with a Jaguar 4x4 Wheel robot as a control platform. We discuss a set of signal processing steps employed in detail and the utility of a regularized logistic regression classifier to detect visual stimuli induced P300 ERPs and, to control the Jaguar robot.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128588472","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 : 2017-01-01DOI: 10.1109/IWW-BCI.2017.7858146
Yiyu Chen, C. Wallraven
Peoples' risk-taking behavior varies from timid and careful, low-risk individuals to bold and careless, high-risk individuals. Can we use EEG to predict who is who? In the present study, we use the balloon analogue risk task (BART) in an EEG experiment in order to find out potential correlates in the EEG signal that allow us to distinguish high risk-takers from low risk-takers. Specifically, we examine the feedback-related negativity components (FRN) in the EEG spectrum and ERP components as potential candidates for such a distinction. Using a sample of 17 participants, we find a reliable, larger FRN for risk avoiders as well as increased delta and theta power in several central electrode sites. These results represent the first step towards robust bio-markers of risk-taking behavior.
{"title":"Pop or not? EEG correlates of risk-taking behavior in the balloon analogue risk task","authors":"Yiyu Chen, C. Wallraven","doi":"10.1109/IWW-BCI.2017.7858146","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858146","url":null,"abstract":"Peoples' risk-taking behavior varies from timid and careful, low-risk individuals to bold and careless, high-risk individuals. Can we use EEG to predict who is who? In the present study, we use the balloon analogue risk task (BART) in an EEG experiment in order to find out potential correlates in the EEG signal that allow us to distinguish high risk-takers from low risk-takers. Specifically, we examine the feedback-related negativity components (FRN) in the EEG spectrum and ERP components as potential candidates for such a distinction. Using a sample of 17 participants, we find a reliable, larger FRN for risk avoiders as well as increased delta and theta power in several central electrode sites. These results represent the first step towards robust bio-markers of risk-taking behavior.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134463172","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.2017.7858158
B. Abibullaev
Brain-Computer Interface (BCI) research hopes to improve the quality of life for people with severe motor disabilities by providing a capability to control external devices using their thoughts. To control a device through BCI, neural signals of a user must be translated to meaningful control commands using various machine learning components, e.g. feature extraction, dimensionality reduction and classification, that should also be carefully designed for practical use. However, the noise and variability in the neural data pose one of the greatest challenges that in practice previously functioning BCI fails in the subsequent operation requiring re-tuning/optimization. This paper presents an idea of defining multiple feature spaces and optimal decision boundaries therein to account for noise and variability in data and improve a generalization of a learning machine. The spaces are defined in the Reproducing Kernel Hilbert Spaces induced by a Radial Basis Gaussian function. Then the learning is done via L1-regularized Support Vector Machines. The central idea behind our approach is that a classifier predicts an unseen test examples by learning more rich feature spaces with a suite of optimal hyperparameters. Empirical evaluation have shown an improved generalization performance (range 79–90%) on two class motor imagery Electroencephalography (EEG) data, when compared with other conventional machine learning methods.
{"title":"Learning suite of kernel feature spaces enhances SMR-based EEG-BCI classification","authors":"B. Abibullaev","doi":"10.1109/IWW-BCI.2017.7858158","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858158","url":null,"abstract":"Brain-Computer Interface (BCI) research hopes to improve the quality of life for people with severe motor disabilities by providing a capability to control external devices using their thoughts. To control a device through BCI, neural signals of a user must be translated to meaningful control commands using various machine learning components, e.g. feature extraction, dimensionality reduction and classification, that should also be carefully designed for practical use. However, the noise and variability in the neural data pose one of the greatest challenges that in practice previously functioning BCI fails in the subsequent operation requiring re-tuning/optimization. This paper presents an idea of defining multiple feature spaces and optimal decision boundaries therein to account for noise and variability in data and improve a generalization of a learning machine. The spaces are defined in the Reproducing Kernel Hilbert Spaces induced by a Radial Basis Gaussian function. Then the learning is done via L1-regularized Support Vector Machines. The central idea behind our approach is that a classifier predicts an unseen test examples by learning more rich feature spaces with a suite of optimal hyperparameters. Empirical evaluation have shown an improved generalization performance (range 79–90%) on two class motor imagery Electroencephalography (EEG) data, when compared with other conventional machine learning methods.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"131 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":"114663090","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.2017.7858147
Kosho Oki, Y. Kurihara, T. Kaburagi, K. Shiba
English is becoming a common language in our global society. Moreover, the verification of English ability is important. The Test of English for International Communication (TOEIC) is representative of a method to estimate English ability quantitatively. However, a significant amount of time is required to take TOEIC. For this reason, an easier measure of English ability is desirable. In this paper, we propose a method to predict English ability from changes in cerebral oxy- and deoxy-hemoglobin (Hb) concentrations by using 10-channel prefrontal cortex near-infrared spectroscopy data at a resting state. The data is obtained when the subjects are solving an English problem. Our proposed system could estimate 11 subjects' TOEIC scores with a 9.06% error rate.
{"title":"English ability score prediction algorithm based on prefrontal cortex blood volume utilizing a regulated linear regression model","authors":"Kosho Oki, Y. Kurihara, T. Kaburagi, K. Shiba","doi":"10.1109/IWW-BCI.2017.7858147","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858147","url":null,"abstract":"English is becoming a common language in our global society. Moreover, the verification of English ability is important. The Test of English for International Communication (TOEIC) is representative of a method to estimate English ability quantitatively. However, a significant amount of time is required to take TOEIC. For this reason, an easier measure of English ability is desirable. In this paper, we propose a method to predict English ability from changes in cerebral oxy- and deoxy-hemoglobin (Hb) concentrations by using 10-channel prefrontal cortex near-infrared spectroscopy data at a resting state. The data is obtained when the subjects are solving an English problem. Our proposed system could estimate 11 subjects' TOEIC scores with a 9.06% error rate.","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":"129746407","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.2017.7858148
Basil Wahn, P. König
Human information processing is limited in capacity. Here, we investigated under which circumstances humans can better process information if they receive task-relevant sensory input via several sensory modalities compared to only one sensory modality (i.e., vision). We found that the benefits of distributing information processing across sensory modalities critically depend on task demands. That is, when information processing requires only spatial processing, distributing information processing across several sensory modalities does not lead to any performance benefits in comparison to receiving the same information only via the visual sensory modality. When information processing additionally involves the discrimination of stimulus attributes, then distributing information processing across several sensory modalities effectively circumvents processing limitations within the visual modality. Crucially, these performance benefits generalize to settings using sensory augmentation as well as a collaborative setting. Findings are potentially applicable to visually taxing real-world tasks that are either performed alone or in a group.
{"title":"Multimodal integration, attention and sensory augmentation?","authors":"Basil Wahn, P. König","doi":"10.1109/IWW-BCI.2017.7858148","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858148","url":null,"abstract":"Human information processing is limited in capacity. Here, we investigated under which circumstances humans can better process information if they receive task-relevant sensory input via several sensory modalities compared to only one sensory modality (i.e., vision). We found that the benefits of distributing information processing across sensory modalities critically depend on task demands. That is, when information processing requires only spatial processing, distributing information processing across several sensory modalities does not lead to any performance benefits in comparison to receiving the same information only via the visual sensory modality. When information processing additionally involves the discrimination of stimulus attributes, then distributing information processing across several sensory modalities effectively circumvents processing limitations within the visual modality. Crucially, these performance benefits generalize to settings using sensory augmentation as well as a collaborative setting. Findings are potentially applicable to visually taxing real-world tasks that are either performed alone or in a group.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"34 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":"128204970","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.2017.7858154
Cuntai Guan, Neethu Robinson, Vikram Shenoy Handiru, V. Prasad
Detection of multiple directional movements could be useful in designing a BCI based upper-limb rehabilitation system for stroke patients. Under the experiment protocol of voluntary right-hand center-out movement in four orthogonal directions, we will discuss how to classify the movement directions and speeds in the spatial-temporal-spectra domains by utilizing regularized wavelet-common spatial pattern, mutual information-based feature selection, adaptive trajectory tracking, and source localization.
{"title":"Detecting and tracking multiple directional movements in EEG based BCI","authors":"Cuntai Guan, Neethu Robinson, Vikram Shenoy Handiru, V. Prasad","doi":"10.1109/IWW-BCI.2017.7858154","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858154","url":null,"abstract":"Detection of multiple directional movements could be useful in designing a BCI based upper-limb rehabilitation system for stroke patients. Under the experiment protocol of voluntary right-hand center-out movement in four orthogonal directions, we will discuss how to classify the movement directions and speeds in the spatial-temporal-spectra domains by utilizing regularized wavelet-common spatial pattern, mutual information-based feature selection, adaptive trajectory tracking, and source localization.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"1 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":"129259893","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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