Pub Date : 2019-02-01DOI: 10.1109/iww-bci.2019.8737312
L. Moctezuma, M. Molinas
This study reports the differences observed in the EEG signals of linguistic activity and resting-state between male and female subjects in a population of 16 individuals (8 females and 8 males). These differences were spotted while performing two experiments: sex identification and subject identification, where the initial aim was to identify the optimal number and placement of EEG channels to obtain high accuracies in sex and subject identification. The results of the identification show that the signals analyzed contain sex-specific information and that the best features from this sex-specific information are extracted from different EEG channel locations and from different hemispheres of the brain, for either sex. The effect of the number of electrodes and electrode localization is seen with clear differences between male and female subjects. The accuracy loss for sex identification when reducing the number of channels from 14 to 1 was of only 0.03 points during resting states (Accuracies from 0.79 to 0.76). For subject identification within either male or female groups during resting states, the accuracy loss was larger when reducing the number of channels from 14 to 1 (0.96 to 0.71 for female, 0.96 to 0.81 for male subjects). One finding of this study is that Theta and Gamma bands are strongest for males in the right hemisphere during resting states, whereas during linguistic activity these bands exhibit similar strengths in the left hemisphere for both males and females. Similar specific features in brain signals may enable the design of a flexible EEG device that can be adapted to specific mental tasks and Subject settings.
{"title":"Sex differences observed in a study of EEG of linguistic activity and resting-state: Exploring optimal EEG channel configurations","authors":"L. Moctezuma, M. Molinas","doi":"10.1109/iww-bci.2019.8737312","DOIUrl":"https://doi.org/10.1109/iww-bci.2019.8737312","url":null,"abstract":"This study reports the differences observed in the EEG signals of linguistic activity and resting-state between male and female subjects in a population of 16 individuals (8 females and 8 males). These differences were spotted while performing two experiments: sex identification and subject identification, where the initial aim was to identify the optimal number and placement of EEG channels to obtain high accuracies in sex and subject identification. The results of the identification show that the signals analyzed contain sex-specific information and that the best features from this sex-specific information are extracted from different EEG channel locations and from different hemispheres of the brain, for either sex. The effect of the number of electrodes and electrode localization is seen with clear differences between male and female subjects. The accuracy loss for sex identification when reducing the number of channels from 14 to 1 was of only 0.03 points during resting states (Accuracies from 0.79 to 0.76). For subject identification within either male or female groups during resting states, the accuracy loss was larger when reducing the number of channels from 14 to 1 (0.96 to 0.71 for female, 0.96 to 0.81 for male subjects). One finding of this study is that Theta and Gamma bands are strongest for males in the right hemisphere during resting states, whereas during linguistic activity these bands exhibit similar strengths in the left hemisphere for both males and females. Similar specific features in brain signals may enable the design of a flexible EEG device that can be adapted to specific mental tasks and Subject settings.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026408","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737264
N. Mrachacz‐Kersting, D. Farina
Here we present the possibility of inducing significant neuroplasticity as assessed by non-invasive transcranial magnetic stimulation (TMS) using a unique Brain-Computer Interface (BCI) build on known mechanisms of memory and learning. This BCI associates in time the cortical signals generated when a stroke patient attempts to perform a movement, and the artificial production of that movement. As for healthy participants, both chronic and sub-acute patients show neuroplastic changes following exposure to this BCI, that is accompanied by significant improvements in function as assessed by clinical scales. The relatively short duration of each intervention session, the fact that it does not require user training or residual muscle activity makes this a viable tool for the clinical setting and my pave the way for future BCIs in the clinic.
{"title":"Modulation of Cortical Excitability with BCI for Stroke Rehabilitation","authors":"N. Mrachacz‐Kersting, D. Farina","doi":"10.1109/IWW-BCI.2019.8737264","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737264","url":null,"abstract":"Here we present the possibility of inducing significant neuroplasticity as assessed by non-invasive transcranial magnetic stimulation (TMS) using a unique Brain-Computer Interface (BCI) build on known mechanisms of memory and learning. This BCI associates in time the cortical signals generated when a stroke patient attempts to perform a movement, and the artificial production of that movement. As for healthy participants, both chronic and sub-acute patients show neuroplastic changes following exposure to this BCI, that is accompanied by significant improvements in function as assessed by clinical scales. The relatively short duration of each intervention session, the fact that it does not require user training or residual muscle activity makes this a viable tool for the clinical setting and my pave the way for future BCIs in the clinic.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134389056","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737328
S. Prabhakar, H. Rajaguru, Seong-Whan Lee
Different pathological and physiological activities of the brain can be analyzed by means of utilizing Electroencephalography (EEG) signals. One such important activity which can be assessed and understood with the help of electrical representation of the brain signals is alcoholism. Alcoholism is a serious concern to many in the world as it affects the vital organs of the human body like liver, brain, lungs, heart, blood, immunity levels etc. In the arena of biomedical research, classification of alcoholic subjects from EEG signals is quite a challenging task. In this paper, the alcoholic EEG signals are analyzed comprehensively for a single alcoholic patient and it is classified with many post classifiers. Initially Correlation Dimension features are extracted from the EEG signals and then it is classified with the help of Detrend Fluctuation Analysis (DFA). In order to improve the classification accuracy further, it is again classified with 6 other post classifiers such as Linear Discriminant Analysis (LDA), Kernel LDA, Firefly algorithm, Gaussian Mixture Model (GMM), Logistic Regression (LR) and Softmax Discriminant Classifier (SDC). Results report a high classification accuracy of 97.91% when GMM is employed followed by a classification accuracy of 97.33% when Logistic Regression is employed. A comparatively low classification accuracy of 89.6% is obtained when LDA was employed.
{"title":"A Comprehensive Analysis of Alcoholic EEG Signals with Detrend Fluctuation Analysis and Post Classifiers","authors":"S. Prabhakar, H. Rajaguru, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2019.8737328","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737328","url":null,"abstract":"Different pathological and physiological activities of the brain can be analyzed by means of utilizing Electroencephalography (EEG) signals. One such important activity which can be assessed and understood with the help of electrical representation of the brain signals is alcoholism. Alcoholism is a serious concern to many in the world as it affects the vital organs of the human body like liver, brain, lungs, heart, blood, immunity levels etc. In the arena of biomedical research, classification of alcoholic subjects from EEG signals is quite a challenging task. In this paper, the alcoholic EEG signals are analyzed comprehensively for a single alcoholic patient and it is classified with many post classifiers. Initially Correlation Dimension features are extracted from the EEG signals and then it is classified with the help of Detrend Fluctuation Analysis (DFA). In order to improve the classification accuracy further, it is again classified with 6 other post classifiers such as Linear Discriminant Analysis (LDA), Kernel LDA, Firefly algorithm, Gaussian Mixture Model (GMM), Logistic Regression (LR) and Softmax Discriminant Classifier (SDC). Results report a high classification accuracy of 97.91% when GMM is employed followed by a classification accuracy of 97.33% when Logistic Regression is employed. A comparatively low classification accuracy of 89.6% is obtained when LDA was employed.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133116132","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737310
M. Grosse-Wentrup
I review efforts to establish communication with completely locked-in patients in the late stage of amyotrophic lateral sclerosis through brain-computer interfacing, and discuss potential explanations for the apparent inability of this patient group to make use of a brain-computer interface. In particular, I argue that disease progression in amyotrophic lateral sclerosis is accompanied by a broad range of neurophysiological- and cognitive changes, which must be taken into account when designing brain-computer interfaces for this patient group.
{"title":"The Elusive Goal of BCI-based Communication with CLIS-ALS Patients","authors":"M. Grosse-Wentrup","doi":"10.1109/IWW-BCI.2019.8737310","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737310","url":null,"abstract":"I review efforts to establish communication with completely locked-in patients in the late stage of amyotrophic lateral sclerosis through brain-computer interfacing, and discuss potential explanations for the apparent inability of this patient group to make use of a brain-computer interface. In particular, I argue that disease progression in amyotrophic lateral sclerosis is accompanied by a broad range of neurophysiological- and cognitive changes, which must be taken into account when designing brain-computer interfaces for this patient group.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122945686","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737338
A. Kübler, L. Botrel
The so-called P300-BCI provided high information transfer rates if stimuli are presented in the visual modality in healthy participants and those with severe neurological disease alike. Visual presentation of stimuli constitutes a severe obstacle to those with no control of eye movement. The following study investigated a potential alternative to using any sensory modality for stimulation. Instead healthy participants were instructed to imagine the traditional flashing matrix. To facilitate such imagery, stimuli were first presented sequentially in the visual and auditory domain. In Experiment 1 (N = 10) we found a decline in performance when stimuli were presented sequentially as compared to randomly, but still a P300 was identifiable albeit lower in amplitude. In Experiment 2 (N = 23) the matrix was still presented as a support, but no visual stimulation occurred. Instead the stimulation frequency was indicated by auditory clicks in condition 1 and removed altogether in condition 2. The P300 amplitude was significantly higher in target than non-target stimulations in both conditions, and higher in the no stimulation condition than in the auditory condition. Selection accuracy was above chance level for 8 participants in the auditory and for 10 in the no stimulation condition. Taken together results indicate that a P300 can be generated by imagined stimulation, but the paradigm requires further investigation and improvement.
{"title":"Imagining the P300 Speller: Good idea or nonsense?","authors":"A. Kübler, L. Botrel","doi":"10.1109/IWW-BCI.2019.8737338","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737338","url":null,"abstract":"The so-called P300-BCI provided high information transfer rates if stimuli are presented in the visual modality in healthy participants and those with severe neurological disease alike. Visual presentation of stimuli constitutes a severe obstacle to those with no control of eye movement. The following study investigated a potential alternative to using any sensory modality for stimulation. Instead healthy participants were instructed to imagine the traditional flashing matrix. To facilitate such imagery, stimuli were first presented sequentially in the visual and auditory domain. In Experiment 1 (N = 10) we found a decline in performance when stimuli were presented sequentially as compared to randomly, but still a P300 was identifiable albeit lower in amplitude. In Experiment 2 (N = 23) the matrix was still presented as a support, but no visual stimulation occurred. Instead the stimulation frequency was indicated by auditory clicks in condition 1 and removed altogether in condition 2. The P300 amplitude was significantly higher in target than non-target stimulations in both conditions, and higher in the no stimulation condition than in the auditory condition. Selection accuracy was above chance level for 8 participants in the auditory and for 10 in the no stimulation condition. Taken together results indicate that a P300 can be generated by imagined stimulation, but the paradigm requires further investigation and improvement.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129696065","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737321
K. Müller
In this short abstract I will discuss recent directions where deep learning is used for analysing brain imaging data, both in the context of BCI and fMRI – summarizing steps taken by the BBCI team and co-workers. It is the nature of this short text that many pointers to research are given all of which show a high overlap to prior own contributions (this is not only unavoidable but intentional) or will touch upon ongoing unpublished respectively pre-published work.
{"title":"Explainable Deep Learning for Analysing Brain Data","authors":"K. Müller","doi":"10.1109/IWW-BCI.2019.8737321","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737321","url":null,"abstract":"In this short abstract I will discuss recent directions where deep learning is used for analysing brain imaging data, both in the context of BCI and fMRI – summarizing steps taken by the BBCI team and co-workers. It is the nature of this short text that many pointers to research are given all of which show a high overlap to prior own contributions (this is not only unavoidable but intentional) or will touch upon ongoing unpublished respectively pre-published work.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134534985","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737350
Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, In-Nea Wang, Seong-Whan Lee, Dong-Joo Kim
Brain computer interface (BCI) could be useful in improving the quality of life for paralyzed patients. Motor imagery classification has recently been a center of research interest in the BCI-based rehabilitation. As of current, spatial features and spectral features were often used independently for motor imagery classification. While few studies attempted to combine the information from varying domains including spectral, spatial and temporal feature, the attempts employed simplistic linear models. In this study, a novel feature extraction method for including spatial and temporal information is proposed. The method uses recurrent convolutional neural network (RCNN) which excels in temporal and spatial classification. The method was tested for classifying wrist twisting-related task classification during manipulation of robotic arm via electroencephalography, and the performance of the method was compared to the conventional motor imagery classifiers with common spatial pattern (CSP) filter. The proposed method showed 73.9% accuracy in the classification of three types of tasks, whereas the highest accuracy achieved by conventional models was 59.5%. Overall, the performance of the proposed RCNN model was greater than the conventional models using the CSP as input features. The findings warrant further application of the proposed methods in varying BCI environment.
{"title":"Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification","authors":"Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, In-Nea Wang, Seong-Whan Lee, Dong-Joo Kim","doi":"10.1109/IWW-BCI.2019.8737350","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737350","url":null,"abstract":"Brain computer interface (BCI) could be useful in improving the quality of life for paralyzed patients. Motor imagery classification has recently been a center of research interest in the BCI-based rehabilitation. As of current, spatial features and spectral features were often used independently for motor imagery classification. While few studies attempted to combine the information from varying domains including spectral, spatial and temporal feature, the attempts employed simplistic linear models. In this study, a novel feature extraction method for including spatial and temporal information is proposed. The method uses recurrent convolutional neural network (RCNN) which excels in temporal and spatial classification. The method was tested for classifying wrist twisting-related task classification during manipulation of robotic arm via electroencephalography, and the performance of the method was compared to the conventional motor imagery classifiers with common spatial pattern (CSP) filter. The proposed method showed 73.9% accuracy in the classification of three types of tasks, whereas the highest accuracy achieved by conventional models was 59.5%. Overall, the performance of the proposed RCNN model was greater than the conventional models using the CSP as input features. The findings warrant further application of the proposed methods in varying BCI environment.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116032336","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 : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737306
Seung-Pyo Seo, Min-Ho Lee, J. Williamson, Seong-Whan Lee
Long duration usage of BCI systems may induce a loss of attention in the participant and result in a decrease of system performance. Therefore, investigation of fatigue during longtime usage and its effect on the signal quality are necessary for the use of BCI systems in daily life. In this study, 54 participants used BCI systems for about five hours, and it included the three major BCI paradigms. Participants conducted each paradigm once again at the end of the experiment. We investigated how fatigue changes as the experiment progresses and report the effect of fatigue on signal quality by comparing the first and second sessions. In the result, a significant increase was seen in questionnaire scores as well as in alpha-band power in the resting state. The signal quality decreased slightly in the MI and SSVEP paradigms, but the amplitude of the P300 in the ERP paradigm increased.
{"title":"Changes in Fatigue and EEG Amplitude during a Longtime Use of Brain-Computer Interface","authors":"Seung-Pyo Seo, Min-Ho Lee, J. Williamson, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2019.8737306","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737306","url":null,"abstract":"Long duration usage of BCI systems may induce a loss of attention in the participant and result in a decrease of system performance. Therefore, investigation of fatigue during longtime usage and its effect on the signal quality are necessary for the use of BCI systems in daily life. In this study, 54 participants used BCI systems for about five hours, and it included the three major BCI paradigms. Participants conducted each paradigm once again at the end of the experiment. We investigated how fatigue changes as the experiment progresses and report the effect of fatigue on signal quality by comparing the first and second sessions. In the result, a significant increase was seen in questionnaire scores as well as in alpha-band power in the resting state. The signal quality decreased slightly in the MI and SSVEP paradigms, but the amplitude of the P300 in the ERP paradigm increased.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121014148","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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