Pub Date : 2019-02-01DOI: 10.1109/IWW-BCI.2019.8737308
Young-Tak Kim, Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, Seong-Whan Lee, Dong-Joo Kim
Motor imagery-based brain-computer interface (BCI) has been widely used to translate user’s motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.
{"title":"Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping","authors":"Young-Tak Kim, Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, Seong-Whan Lee, Dong-Joo Kim","doi":"10.1109/IWW-BCI.2019.8737308","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737308","url":null,"abstract":"Motor imagery-based brain-computer interface (BCI) has been widely used to translate user’s motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"16 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":"133560358","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.2019.8737307
P. Douglas, D. Douglas
Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive, it also represents an excellent candidate modality for use in the BCI setting. Nonetheless, a complete understanding of how EEG measurements (voltage fluctuations) relate to information processing in the brain remains somewhat elusive. A deeper understanding of the neuroanatomical underpinnings of the EEG signal may help explain inter-individual variability in evoked and induced potentials, which may improve BCI therapies targeted to the individual. According to classic biophysical models, EEG fluctuations are primarily a reflection of locally synchronized neuronal oscillations within the gray matter oriented approximately orthogonal to the scalp. In contrast, global models ignore local signals due to dendritic processing, and suggest that propagation delays due to white matter architecture are responsible for the EEG signal, and are capable of explaining the coherence between numerous rhythms (e.g., alpha) at spatially distinct areas of the scalp. Recently, combined local-global models suggest that the EEG signal may reflect a superposition of local processing along with global contributors including transduction along white matter tracts in the brain. Incorporating both local and global (e.g., white matter) priors into EEG source models may therefore improve source estimates. These models may also help disentangle which aspects of the EEG signal are predicted to colocalize spatially with measurements from functional MRI (fMRI). Here, we explore the possibility that white matter conductivity contributes to EEG measurements via a generative model based on classic axonal transduction models, and discuss its potential implications for source estimation.
{"title":"Reconsidering Spatial Priors In EEG Source Estimation : Does White Matter Contribute to EEG Rhythms?","authors":"P. Douglas, D. Douglas","doi":"10.1109/IWW-BCI.2019.8737307","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737307","url":null,"abstract":"Electroencephalogram (EEG) has been a core tool used in functional neuroimaging in humans for nearly a hundred years. Because it is inexpensive, easy to implement, and noninvasive, it also represents an excellent candidate modality for use in the BCI setting. Nonetheless, a complete understanding of how EEG measurements (voltage fluctuations) relate to information processing in the brain remains somewhat elusive. A deeper understanding of the neuroanatomical underpinnings of the EEG signal may help explain inter-individual variability in evoked and induced potentials, which may improve BCI therapies targeted to the individual. According to classic biophysical models, EEG fluctuations are primarily a reflection of locally synchronized neuronal oscillations within the gray matter oriented approximately orthogonal to the scalp. In contrast, global models ignore local signals due to dendritic processing, and suggest that propagation delays due to white matter architecture are responsible for the EEG signal, and are capable of explaining the coherence between numerous rhythms (e.g., alpha) at spatially distinct areas of the scalp. Recently, combined local-global models suggest that the EEG signal may reflect a superposition of local processing along with global contributors including transduction along white matter tracts in the brain. Incorporating both local and global (e.g., white matter) priors into EEG source models may therefore improve source estimates. These models may also help disentangle which aspects of the EEG signal are predicted to colocalize spatially with measurements from functional MRI (fMRI). Here, we explore the possibility that white matter conductivity contributes to EEG measurements via a generative model based on classic axonal transduction models, and discuss its potential implications for source estimation.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"29 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":"132539784","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.2019.8737256
Amardeep Singh, Sunil Lal, H. Guesgen
Motor imagery based brain computer interface requires large number of labeled subject specific training trials to calibrate system for new subjects. This is due to huge variations in individual characteristics. Major challenge in development of brain computer interface is to reduce calibration time or completely eliminate. Existing approaches rise up to this challenge by incorporating Euclidean representation of the individual variations from other subjects’ trials. They use covariance matrices from other subjects but do not consider the geometry of the covariance matrices, which lies in space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing Riemannian approach by incorporating geometrical properties of covariance matrices in the subject to subject transfer. Our method outperforms the state of the art methods on the BCI competition dataset IVa. Our proposed method yielded accuracy of 77.67%, 100%, 75%, 87.05% and 91.67% for five subjects (aa, al, av, aw and ay respectively) in the dataset resulting in an average accuracy of 86.27%.
{"title":"Motor Imagery Classification Based on Subject to Subject Transfer in Riemannian Manifold","authors":"Amardeep Singh, Sunil Lal, H. Guesgen","doi":"10.1109/IWW-BCI.2019.8737256","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737256","url":null,"abstract":"Motor imagery based brain computer interface requires large number of labeled subject specific training trials to calibrate system for new subjects. This is due to huge variations in individual characteristics. Major challenge in development of brain computer interface is to reduce calibration time or completely eliminate. Existing approaches rise up to this challenge by incorporating Euclidean representation of the individual variations from other subjects’ trials. They use covariance matrices from other subjects but do not consider the geometry of the covariance matrices, which lies in space of Symmetric Positive Definite (SPD) matrices. This inevitably limits their performance. We focus on reducing calibration time by introducing Riemannian approach by incorporating geometrical properties of covariance matrices in the subject to subject transfer. Our method outperforms the state of the art methods on the BCI competition dataset IVa. Our proposed method yielded accuracy of 77.67%, 100%, 75%, 87.05% and 91.67% for five subjects (aa, al, av, aw and ay respectively) in the dataset resulting in an average accuracy of 86.27%.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"65 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":"123837237","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.2019.8737305
Jisung Park, Sung-Phil Kim
The current neural decoding algorithms for brain-machine interfaces (BMIs) have largely focused on predicting the velocity of arm movements from neuronal ensemble activity. Yet, mounting evidence indicates that velocity is encoded separately in motor cortical activity. In this regard, we aimed to decode separate speed and direction information independently using a machine learning algorithm based on long short-term memory (LSTM). The performance of the proposed decoder was compared with the traditional decodres using velocity Kalman filter and the velocity LSTM. The proposed decoder showed better angular prediction than the other decoders. Also, the reconstruction hand trajectories with the proposed decoder acquired the targets more often. Movement time of the reconstructed trajectories by the proposed decoder was shorter than the others. Our results suggest advantages of decoding speed and direction independently using a nonlinear model such as LSTM for intracortical BMIs.
{"title":"Estimation of speed and direction of arm movements from M1 activity using a nonlinear neural decoder","authors":"Jisung Park, Sung-Phil Kim","doi":"10.1109/IWW-BCI.2019.8737305","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737305","url":null,"abstract":"The current neural decoding algorithms for brain-machine interfaces (BMIs) have largely focused on predicting the velocity of arm movements from neuronal ensemble activity. Yet, mounting evidence indicates that velocity is encoded separately in motor cortical activity. In this regard, we aimed to decode separate speed and direction information independently using a machine learning algorithm based on long short-term memory (LSTM). The performance of the proposed decoder was compared with the traditional decodres using velocity Kalman filter and the velocity LSTM. The proposed decoder showed better angular prediction than the other decoders. Also, the reconstruction hand trajectories with the proposed decoder acquired the targets more often. Movement time of the reconstructed trajectories by the proposed decoder was shorter than the others. Our results suggest advantages of decoding speed and direction independently using a nonlinear model such as LSTM for intracortical BMIs.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"88 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":"115312720","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.2019.8737250
{"title":"BCI 2019 Welcome Message from the General Chairs","authors":"","doi":"10.1109/iww-bci.2019.8737250","DOIUrl":"https://doi.org/10.1109/iww-bci.2019.8737250","url":null,"abstract":"","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"88 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":"116656483","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.2019.8737263
Soren Moller Christensen, Nicklas Stubkjær Holm, S. Puthusserypady
Worldwide, millions of people are locked in or in a wheelchair, due to several neuromuscular disorders or spinal cord injuries. These individuals are deprived of trivial social activities, like interacting or playing games with other people. Such activities are crucial for personal development, and can have a great impact on the quality of their lives. This work aims at the design and implementation of an electroencephalography (EEG) based motor imagery (MI) brain computer interface (BCI) system that would allow disabled, and able-bodied, individuals alike to control a drone in a 3D physical environment by only using their thoughts. An improved version of the filter bank common spatial pattern (FBCSP) algorithm was developed, and it has shown to perform superior (68.5% accuracy) to the winning FBCSP algorithm (67.8% accuracy), when tested on dataset 2a (4 class MI) of the BCI competition IV. A deep convolutional neural network (CNN) based algorithm was also implemented and tested on the same dataset, which however performed inferior (62.9% accuracy) to the winner, as well as our proposed FBCSP algorithms. The improved FBCSP was then tested on our in-house 5-class (left hand, right hand, tongue, both feet and rest) MI dataset (collected from 10 able-bodied subjects) and obtained a mean accuracy of 41.8±11.74%. This is considered a significant result though it is not good enough to attempt the control of a real drone.
{"title":"An Improved Five Class MI Based BCI Scheme for Drone Control Using Filter Bank CSP","authors":"Soren Moller Christensen, Nicklas Stubkjær Holm, S. Puthusserypady","doi":"10.1109/IWW-BCI.2019.8737263","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737263","url":null,"abstract":"Worldwide, millions of people are locked in or in a wheelchair, due to several neuromuscular disorders or spinal cord injuries. These individuals are deprived of trivial social activities, like interacting or playing games with other people. Such activities are crucial for personal development, and can have a great impact on the quality of their lives. This work aims at the design and implementation of an electroencephalography (EEG) based motor imagery (MI) brain computer interface (BCI) system that would allow disabled, and able-bodied, individuals alike to control a drone in a 3D physical environment by only using their thoughts. An improved version of the filter bank common spatial pattern (FBCSP) algorithm was developed, and it has shown to perform superior (68.5% accuracy) to the winning FBCSP algorithm (67.8% accuracy), when tested on dataset 2a (4 class MI) of the BCI competition IV. A deep convolutional neural network (CNN) based algorithm was also implemented and tested on the same dataset, which however performed inferior (62.9% accuracy) to the winner, as well as our proposed FBCSP algorithms. The improved FBCSP was then tested on our in-house 5-class (left hand, right hand, tongue, both feet and rest) MI dataset (collected from 10 able-bodied subjects) and obtained a mean accuracy of 41.8±11.74%. This is considered a significant result though it is not good enough to attempt the control of a real drone.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"38 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":"128549729","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.2019.8737319
Jaehyung Lee, Kabmun Cha, Hyungmin Kim, Junhyuk Choi, Choong Hyun Kim, S. Lee
The goal of this study was to compare decoding accuracy of left and right movement intention from electroencephalography (EEG) using three different types of paradigms: Motor Imagery (MI), Selective Attention (SA), and Hybrid task (HY)). Specifically, SA and HY are the Steady-State Somatosensory Evoked potential (SSSEP) paradigms which elicit brain responses to tactile stimulation. One subject participated in two sessions (Screening and Study session). In the screening session, resonance-like frequency of the subject was found at each hand while sitting on a chair. In the study session, the subject was asked to imagine either left of right hand open-close movement (MI task), to give selective attention to the vibrotactile stimulation (SA task), and to perform combined MI and SA task (HY) according to a randomly assigned directional cue. The accuracies of 3 paradigms were MI-left 65.8%, MI-right 69.2% (mean: 67.5%), SA-left 76.6%, SA-right 84.0% (mean: 80.3%) and HY-left 93.8%, HY-right 95.9% (mean: 94.9%). The method and results of the current study could be a basis for controlling the left and right movement direction of an exoskeleton robot using EEG.
{"title":"Hybrid MI-SSSEP Paradigm for classifying left and right movement toward BCI for exoskeleton control","authors":"Jaehyung Lee, Kabmun Cha, Hyungmin Kim, Junhyuk Choi, Choong Hyun Kim, S. Lee","doi":"10.1109/IWW-BCI.2019.8737319","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737319","url":null,"abstract":"The goal of this study was to compare decoding accuracy of left and right movement intention from electroencephalography (EEG) using three different types of paradigms: Motor Imagery (MI), Selective Attention (SA), and Hybrid task (HY)). Specifically, SA and HY are the Steady-State Somatosensory Evoked potential (SSSEP) paradigms which elicit brain responses to tactile stimulation. One subject participated in two sessions (Screening and Study session). In the screening session, resonance-like frequency of the subject was found at each hand while sitting on a chair. In the study session, the subject was asked to imagine either left of right hand open-close movement (MI task), to give selective attention to the vibrotactile stimulation (SA task), and to perform combined MI and SA task (HY) according to a randomly assigned directional cue. The accuracies of 3 paradigms were MI-left 65.8%, MI-right 69.2% (mean: 67.5%), SA-left 76.6%, SA-right 84.0% (mean: 80.3%) and HY-left 93.8%, HY-right 95.9% (mean: 94.9%). The method and results of the current study could be a basis for controlling the left and right movement direction of an exoskeleton robot using EEG.","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":"124339867","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.2019.8737346
Dongjae Kim, Sang Wan Lee
Despite the fact that a majority of Brain-Computer Interface (BCI) studies have focused on decoding signals related to movement, decoding intention underlying movement might be equally important. Fundamental properties of electroencephalography (EEG) constrain temporal resolutions for directly decoding movement kinematics, so estimating intention pertaining to movement, which would help us predict both long and short term human behaviors, may be an alternative solution for overcoming this issue. To estimate movement intention, recent studies found it helpful to consider hierarchical control between two distinctive learning strategies in reinforcement learning: a goal-directed and a habitual strategy. In the previous study, we suggested a proof of concept, called a model-based BCI framework, that can distinguish different learning strategies from electroencephalography (EEG) data. To advance this concept, we proposed intention decoders based on simple long short-term memory models (LSTM). To test the effect of intention estimation on prediction performance, we trained two versions of intention decoders: one with and the other without making use of underlying learning strategies decoded by the model-based BCI. The simulation results demonstrated that estimation performance of intention decoders with model-based BCI is significantly better (84%) than the one without model-based BCI (77%). We argued that by using model-based BCI, we can not only estimate learning strategy but also improve decoding performance of movement intention with high accuracy.
{"title":"Decoding both intention and learning strategies from EEG signals","authors":"Dongjae Kim, Sang Wan Lee","doi":"10.1109/IWW-BCI.2019.8737346","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737346","url":null,"abstract":"Despite the fact that a majority of Brain-Computer Interface (BCI) studies have focused on decoding signals related to movement, decoding intention underlying movement might be equally important. Fundamental properties of electroencephalography (EEG) constrain temporal resolutions for directly decoding movement kinematics, so estimating intention pertaining to movement, which would help us predict both long and short term human behaviors, may be an alternative solution for overcoming this issue. To estimate movement intention, recent studies found it helpful to consider hierarchical control between two distinctive learning strategies in reinforcement learning: a goal-directed and a habitual strategy. In the previous study, we suggested a proof of concept, called a model-based BCI framework, that can distinguish different learning strategies from electroencephalography (EEG) data. To advance this concept, we proposed intention decoders based on simple long short-term memory models (LSTM). To test the effect of intention estimation on prediction performance, we trained two versions of intention decoders: one with and the other without making use of underlying learning strategies decoded by the model-based BCI. The simulation results demonstrated that estimation performance of intention decoders with model-based BCI is significantly better (84%) than the one without model-based BCI (77%). We argued that by using model-based BCI, we can not only estimate learning strategy but also improve decoding performance of movement intention with high accuracy.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"20 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":"116525040","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.2019.8737326
K. Miller, G. Huiskamp, D. V. Blooijs, D. Hermes, T. Gebbink, C. Ferrier, P. V. Rijen, P. Gosselaar, N. Ramsey, F. Leijten
We present the case of a patient who underwent placement of an electrocorticographic grid for seizure focus localization. Single-pulse electrical stimulation pulses were delivered throughout the grid, and evoked potentials in response to this stimulation were measured from a pre-central gyral, primary motor, electrode. A range of six different general evoked potential responses were observed. Stimulation sites that produced each response type were noted to cluster anatomically, suggesting different potential connectivity motifs between each brain region with primary motor cortex. This observation is an introduction to the presentation KJM will give at the 7th International Winter Conference on Brain-Computer Interface at High1 in Korea, titled “The relationship between task-inferred connectivity and cortico-cortical evoked potentials in human motor cortex.”
{"title":"An observation of anatomical clustering in inputs to primary motor cortex in cortico-cortical brain surface evoked potentials","authors":"K. Miller, G. Huiskamp, D. V. Blooijs, D. Hermes, T. Gebbink, C. Ferrier, P. V. Rijen, P. Gosselaar, N. Ramsey, F. Leijten","doi":"10.1109/IWW-BCI.2019.8737326","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737326","url":null,"abstract":"We present the case of a patient who underwent placement of an electrocorticographic grid for seizure focus localization. Single-pulse electrical stimulation pulses were delivered throughout the grid, and evoked potentials in response to this stimulation were measured from a pre-central gyral, primary motor, electrode. A range of six different general evoked potential responses were observed. Stimulation sites that produced each response type were noted to cluster anatomically, suggesting different potential connectivity motifs between each brain region with primary motor cortex. This observation is an introduction to the presentation KJM will give at the 7th International Winter Conference on Brain-Computer Interface at High1 in Korea, titled “The relationship between task-inferred connectivity and cortico-cortical evoked potentials in human motor cortex.”","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"21 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":"123887556","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.2019.8737262
Ziyu Li, Hailing Wang, Xia Wu, Xueyuan Xu, Shuai Wei, L. Yao
Recent studies have shown that the performance of working memory can be improved by the adaptation and enhancement of EEG neurofeedback training. A multitude of effective neurofeedback indicators have been proposed, most of which are based on single brain region, single rhythm wave. Some studies have also pointed out that the core factor of enhancing memory is the coherence of the rhythm waves between different brain regions, rather than the amplitude or power of single rhythm. Therefore, this study takes the synchronization of brain regions as the starting point, proposed coherence value of theta rhythm wave between anterior and posterior brain region as feedback indicator for neurofeedback training, and the result verified that the brain multi-region based neurofeedback indicator plays an important part for the improvement of working memory ability.
{"title":"Working Memory Training Using EEG Neurofeedback Based on Theta Coherence of Brain Regions","authors":"Ziyu Li, Hailing Wang, Xia Wu, Xueyuan Xu, Shuai Wei, L. Yao","doi":"10.1109/IWW-BCI.2019.8737262","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2019.8737262","url":null,"abstract":"Recent studies have shown that the performance of working memory can be improved by the adaptation and enhancement of EEG neurofeedback training. A multitude of effective neurofeedback indicators have been proposed, most of which are based on single brain region, single rhythm wave. Some studies have also pointed out that the core factor of enhancing memory is the coherence of the rhythm waves between different brain regions, rather than the amplitude or power of single rhythm. Therefore, this study takes the synchronization of brain regions as the starting point, proposed coherence value of theta rhythm wave between anterior and posterior brain region as feedback indicator for neurofeedback training, and the result verified that the brain multi-region based neurofeedback indicator plays an important part for the improvement of working memory ability.","PeriodicalId":345970,"journal":{"name":"2019 7th International Winter Conference on Brain-Computer Interface (BCI)","volume":"35 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":"122509138","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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