Pub Date : 1900-01-01DOI: 10.1109/IWW-BCI.2017.7858174
Young-Jin Kee, Dong-Ok Won, Seong-Whan Lee
Recently, steady-state somatosensory evoked potentials (SSSEPs) which are brain responses to tactile stimulation of specific frequency in somatosensory have been researched in brain-computer interface (BCI) groups. Classification of both feet is important in gait control system. Previous SSSEP studies have mainly researched a feasibility of discrimination by stimulator attached on upper limb (e.g., finger or arm). However, SSSEP-based classification of both feet could be useful in BCI-based gait rehabilitation system. Hence, we investigate a possibility of discrimination of both feet using SSSEP. To this end, we obtain optimal stimuli frequencies in the screening session. In subsequence test session, the optimal stimuli were attached on the left and right foot, respectively. Six healthy subjects conducted the task which was the subjects concentrate on the tactile stimuli following by random visual cue. The classification results show 72.6% and 72.2% in two methods (i.e., common spatial pattern (CSP) and power spectral density (PSD)). Furthermore, we analyzed differences of spatial and spectral features for reliable BCI performance. These results suggest that classification both feet can be available in SSSEP-based BCI for gait rehabilitation.
{"title":"Classification of left and right foot movement intention based on steady-state somatosensory evoked potentials","authors":"Young-Jin Kee, Dong-Ok Won, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2017.7858174","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858174","url":null,"abstract":"Recently, steady-state somatosensory evoked potentials (SSSEPs) which are brain responses to tactile stimulation of specific frequency in somatosensory have been researched in brain-computer interface (BCI) groups. Classification of both feet is important in gait control system. Previous SSSEP studies have mainly researched a feasibility of discrimination by stimulator attached on upper limb (e.g., finger or arm). However, SSSEP-based classification of both feet could be useful in BCI-based gait rehabilitation system. Hence, we investigate a possibility of discrimination of both feet using SSSEP. To this end, we obtain optimal stimuli frequencies in the screening session. In subsequence test session, the optimal stimuli were attached on the left and right foot, respectively. Six healthy subjects conducted the task which was the subjects concentrate on the tactile stimuli following by random visual cue. The classification results show 72.6% and 72.2% in two methods (i.e., common spatial pattern (CSP) and power spectral density (PSD)). Furthermore, we analyzed differences of spatial and spectral features for reliable BCI performance. These results suggest that classification both feet can be available in SSSEP-based BCI for gait rehabilitation.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"39 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":"116631004","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.2017.7858150
K. Miller, D. Hermes
The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. We measured brain surface physiology with implanted electrocorticography (ECoG) electrodes in humans. Physiological responses to visual stimuli in object-specific ventral temporal loci are highly polymorphic in different cortical loci, for both broadband and raw potential trace changes. To address this, we developed a template-projection method, where averaged responses from a localizer task are projected into the continuous datastream recorded from the brain. These projections are used to build a feature space. A classifier for decoding visual perception is applied to this feature space during training periods, and is applied to plain images, as well as noise masked images. This enables robust classification of visual perceptual state.
{"title":"A template-projection approach to decode higher-order vision in realtime and at the perceptual threshold","authors":"K. Miller, D. Hermes","doi":"10.1109/IWW-BCI.2017.7858150","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858150","url":null,"abstract":"The link between object perception and neural activity in visual cortical areas is a problem of fundamental importance in neuroscience. We measured brain surface physiology with implanted electrocorticography (ECoG) electrodes in humans. Physiological responses to visual stimuli in object-specific ventral temporal loci are highly polymorphic in different cortical loci, for both broadband and raw potential trace changes. To address this, we developed a template-projection method, where averaged responses from a localizer task are projected into the continuous datastream recorded from the brain. These projections are used to build a feature space. A classifier for decoding visual perception is applied to this feature space during training periods, and is applied to plain images, as well as noise masked images. This enables robust classification of visual perceptual state.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"308 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":"132235682","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.2017.7858145
H. Heekeren, Stefan Schulreich, Peter N. C. Mohr, C. Morawetz
Emotions have long been suspected to play an important role in decision making, e.g. theoretic approaches propose that both cognitive and affective processes play a role in the valuation of choice alternatives. There are two main mechanisms, how affect can modulate decision-making processes: First, incidental affect, which can be defined as a baseline affective state that is unrelated to the decision, may carry over to the assessment of choice options. Second, emotional reactions to the choice may be incorporated into the assessment of choice options. Crucially, this modulatory relationship between affect and choice is reciprocal: changing emotion can change choices. Here we report results of some of our recent studies characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.
{"title":"How incidental affect and emotion regulation modulate decision making under risk","authors":"H. Heekeren, Stefan Schulreich, Peter N. C. Mohr, C. Morawetz","doi":"10.1109/IWW-BCI.2017.7858145","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858145","url":null,"abstract":"Emotions have long been suspected to play an important role in decision making, e.g. theoretic approaches propose that both cognitive and affective processes play a role in the valuation of choice alternatives. There are two main mechanisms, how affect can modulate decision-making processes: First, incidental affect, which can be defined as a baseline affective state that is unrelated to the decision, may carry over to the assessment of choice options. Second, emotional reactions to the choice may be incorporated into the assessment of choice options. Crucially, this modulatory relationship between affect and choice is reciprocal: changing emotion can change choices. Here we report results of some of our recent studies characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"114 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":"114592068","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.2017.7858168
Bo-Ram Lee, Dong-Ok Won, K. Seo, Hyun Jeong Kim, Seong-Whan Lee
There have been lots of trials to classify a depth of anesthesia using diverse physiological indices. In this study, we classified wakefulness and propofol-induced sedation using combined electroencephalography (EEG) and electrocardiography (ECG) features for better classification performance. We extract each spectral band of EEG and very low frequency (VLF) of heart rate variability using spectrogram and low-pass filter, respectively. We used combined feature of EEG spectral bands and VLF and shrinkage-regularized linear discriminant analysis as a classifier. Our results show that combination of EEG spectral power and VLF can improve the classification performance between wakefulness and sedation from 95.1±5.3% to 96.4±4.2%.
{"title":"Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG","authors":"Bo-Ram Lee, Dong-Ok Won, K. Seo, Hyun Jeong Kim, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2017.7858168","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858168","url":null,"abstract":"There have been lots of trials to classify a depth of anesthesia using diverse physiological indices. In this study, we classified wakefulness and propofol-induced sedation using combined electroencephalography (EEG) and electrocardiography (ECG) features for better classification performance. We extract each spectral band of EEG and very low frequency (VLF) of heart rate variability using spectrogram and low-pass filter, respectively. We used combined feature of EEG spectral bands and VLF and shrinkage-regularized linear discriminant analysis as a classifier. Our results show that combination of EEG spectral power and VLF can improve the classification performance between wakefulness and sedation from 95.1±5.3% to 96.4±4.2%.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"26 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":"121924065","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.2017.7858165
Jong-Pil Kim, Seong-Whan Lee
Driver's mental work load has been known for one of the significant causes of traffic accidents in driving situations. Hence, in this study, we investigate the effects of mental work load on brain activity during an emergency situation in driving simulator. We compare the differences of electroencephalography (EEG) signals between emergency situations without- and with mental work load on simulated driving. Visual stimuli for emergency situation and auditory stimuli for mental work load situation were presented independently and simultaneously. We used regularized linear discriminant analysis (RLDA) for classifying event-related potentials (ERPs) on mental events which are related to brain activity in time domain. The classification results in the emergency situations with the mental work load were significantly reduced as compared with in the only emergency situations.
{"title":"Time domain EEG analysis for evaluating the effects of driver's mental work load during simulated driving","authors":"Jong-Pil Kim, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2017.7858165","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858165","url":null,"abstract":"Driver's mental work load has been known for one of the significant causes of traffic accidents in driving situations. Hence, in this study, we investigate the effects of mental work load on brain activity during an emergency situation in driving simulator. We compare the differences of electroencephalography (EEG) signals between emergency situations without- and with mental work load on simulated driving. Visual stimuli for emergency situation and auditory stimuli for mental work load situation were presented independently and simultaneously. We used regularized linear discriminant analysis (RLDA) for classifying event-related potentials (ERPs) on mental events which are related to brain activity in time domain. The classification results in the emergency situations with the mental work load were significantly reduced as compared with in the only emergency situations.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"46 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":"124310674","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.2017.7858144
Netiwit Kaongoen, Sungho Jo
Brain-computer interfaces (BCIs) that utilize auditory stimuli have been designed to support users or patients with visual impairment that are incapable of using the conventional visual-based BCI. As an alternative to auditory P300-based BCI, researchers have reported the possibility of using the auditory steady state response (ASSR) in the binary-class BCI system. In the present work, we investigated the effect of selective attention on the amplitude of ASSRs when three ASSR stimuli are simultaneously given. The result shows that the amplitude of ASSR is significantly increased by approximately 20% when the subject selectively attend to the target stimulus. There is also no difference in the effect of selective attention between when two stimuli and three stimuli are given. This current work suggests the possibility of incooperating ASSR into the auditory BCI system that deal with multiple-class problem.
{"title":"The effect of selective attention on multiple ASSRs for future BCI application","authors":"Netiwit Kaongoen, Sungho Jo","doi":"10.1109/IWW-BCI.2017.7858144","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858144","url":null,"abstract":"Brain-computer interfaces (BCIs) that utilize auditory stimuli have been designed to support users or patients with visual impairment that are incapable of using the conventional visual-based BCI. As an alternative to auditory P300-based BCI, researchers have reported the possibility of using the auditory steady state response (ASSR) in the binary-class BCI system. In the present work, we investigated the effect of selective attention on the amplitude of ASSRs when three ASSR stimuli are simultaneously given. The result shows that the amplitude of ASSR is significantly increased by approximately 20% when the subject selectively attend to the target stimulus. There is also no difference in the effect of selective attention between when two stimuli and three stimuli are given. This current work suggests the possibility of incooperating ASSR into the auditory BCI system that deal with multiple-class problem.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"57 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":"126546112","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.2017.7858166
S. Prabhakar, H. Rajaguru
In the past two decades, the Electroencephalograph (EEG) dependent Brain Computer Interface (BCI) for analyzing and detecting the mental disorders especially epilepsy has triggered a lot of research interest in both biomedical industrial side and academia. The main ingredient of EEG dependent BCI are preprocessing of EEG signals, feature extraction of EEG signals and classification of EEG signals. Very rich and useful information about the electrical activities of the brain is provided by the EEG. The amplitude and frequency varies in the EEG signal when various mental tasks are executed. Due to the lengthy nature of the EEG data, computing it becomes quite hectic. Therefore in this paper, the dimensions of the lengthy EEG recorded data is reduced with the help of Principal Component Analysis (PCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Singular Value Decomposition (SVD) and Power Spectral Density (PSD). After reducing the dimensions, the new obtained dimensionally reduced values are classified to get the epilepsy risk level from EEG signals with the help of a probabilistic model called Gaussian Mixture Model (GMM). The result analysis is performed with the benchmark terms like Performance Index, Accuracy, Quality Value and Time Delay. The most promising result in this study shows that when PSD is implemented as a dimensionality reduction technique and when classified with GMM, an average high accuracy of 97.46% is attained along with an average Performance Index of 94.69%.
{"title":"Conceptual analysis of epilepsy classification using probabilistic mixture models","authors":"S. Prabhakar, H. Rajaguru","doi":"10.1109/IWW-BCI.2017.7858166","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858166","url":null,"abstract":"In the past two decades, the Electroencephalograph (EEG) dependent Brain Computer Interface (BCI) for analyzing and detecting the mental disorders especially epilepsy has triggered a lot of research interest in both biomedical industrial side and academia. The main ingredient of EEG dependent BCI are preprocessing of EEG signals, feature extraction of EEG signals and classification of EEG signals. Very rich and useful information about the electrical activities of the brain is provided by the EEG. The amplitude and frequency varies in the EEG signal when various mental tasks are executed. Due to the lengthy nature of the EEG data, computing it becomes quite hectic. Therefore in this paper, the dimensions of the lengthy EEG recorded data is reduced with the help of Principal Component Analysis (PCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Singular Value Decomposition (SVD) and Power Spectral Density (PSD). After reducing the dimensions, the new obtained dimensionally reduced values are classified to get the epilepsy risk level from EEG signals with the help of a probabilistic model called Gaussian Mixture Model (GMM). The result analysis is performed with the benchmark terms like Performance Index, Accuracy, Quality Value and Time Delay. The most promising result in this study shows that when PSD is implemented as a dimensionality reduction technique and when classified with GMM, an average high accuracy of 97.46% is attained along with an average Performance Index of 94.69%.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"28 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":"133614301","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.2017.7858143
Duaa AlQattan, F. Sepulveda
While BCIs have a wide range of applications, the majority of research in the field is concentrated on addressing the issues of controlling and communicating for paralysed patients. This research seeks to examine—through the completion of offline experimentation—a particular aspect; that is, the likelihood of linguistic communication with those paralysed patients, merely by means of neural activity in the brain. Electroencephalogram (EEG) brain activities obtained whilst imagining execution of six one-handed signs from American Sign Language (ASL) were investigated. Upon reviewing the findings, it is demonstrated that EEG signal analysis can be used efficiently to identify hand movement of sign language from the brain. SVM and LDA both showed the highest accuracy, achieving around 75% correct when the Entropy feature type was examined.
{"title":"Towards sign language recognition using EEG-based motor imagery brain computer interface","authors":"Duaa AlQattan, F. Sepulveda","doi":"10.1109/IWW-BCI.2017.7858143","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858143","url":null,"abstract":"While BCIs have a wide range of applications, the majority of research in the field is concentrated on addressing the issues of controlling and communicating for paralysed patients. This research seeks to examine—through the completion of offline experimentation—a particular aspect; that is, the likelihood of linguistic communication with those paralysed patients, merely by means of neural activity in the brain. Electroencephalogram (EEG) brain activities obtained whilst imagining execution of six one-handed signs from American Sign Language (ASL) were investigated. Upon reviewing the findings, it is demonstrated that EEG signal analysis can be used efficiently to identify hand movement of sign language from the brain. SVM and LDA both showed the highest accuracy, achieving around 75% correct when the Entropy feature type was examined.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"145 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":"132128766","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.2017.7858161
Hyuk-soo Shin, Wonzoo Chung
In this paper, we present a novel motor imagery classification method in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) using forced whitened sample covariance matrices as features. The proposed method performs a constant-forcing to the weaker sources of covariance matrices before a whitening process to prevent amplifications of noise sources which have small power relative to class relevant sources. Experimental results show the improved accuracy in comparison with a classification without forced whitening process.
{"title":"Brain computer interface approach using sensor covariance matrix with forced whitening","authors":"Hyuk-soo Shin, Wonzoo Chung","doi":"10.1109/IWW-BCI.2017.7858161","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858161","url":null,"abstract":"In this paper, we present a novel motor imagery classification method in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) using forced whitened sample covariance matrices as features. The proposed method performs a constant-forcing to the weaker sources of covariance matrices before a whitening process to prevent amplifications of noise sources which have small power relative to class relevant sources. Experimental results show the improved accuracy in comparison with a classification without forced whitening process.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"135 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":"133584231","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.2017.7858169
L. Ngo, Jae‐Ho Han
Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-the-art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage in diagnosing ophthalmological disease or pathology.
{"title":"Advanced deep learning for blood vessel segmentation in retinal fundus images","authors":"L. Ngo, Jae‐Ho Han","doi":"10.1109/IWW-BCI.2017.7858169","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2017.7858169","url":null,"abstract":"Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-the-art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage in diagnosing ophthalmological disease or pathology.","PeriodicalId":443427,"journal":{"name":"2017 5th International Winter Conference on Brain-Computer Interface (BCI)","volume":"5 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":"126145638","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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