Pub Date : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311502
Seung Tae Yang, S. Jin, Gihyoun Lee, Seon Yun Jeong, J. An
Recently, only little is known about the cortical activity of tactile sensation for the dominant and subdominant hand. The objective of this study was to investigate the hemodynamic response of human's cortical brain to tactile sensation to compare the dominant and subdominant hand. Ten healthy adults, 25–35 ages, were enrolled. A 45 channel near-infrared spectroscopy system was used to measure brain responses and a solenoid resonance actuator was utilized to stimulate tactile sensation. The results showed that for the hemodynamic response to both hands on tactile stimulation, the corresponding primary sensory cortex and supplementary motor area were commonly activated, but the tactile stimuli of the subdominant hand induced broader areas of cortical activation than that of the dominant hand. Thus, broad brain areas, including the primary motor cortex and sensory association cortex, were activated by tactile stimulation in subdominant hand. These results suggest that there are differences in brain responses to tactile stimulation of the dominant and subdominant hand, which may reflect the importance of neural adaptability and efficiency for tactile sensation of the hand dominance.
{"title":"Dominant and subdominant hand exhibit different cortical activation patterns during tactile stimulation: An fNIRS study","authors":"Seung Tae Yang, S. Jin, Gihyoun Lee, Seon Yun Jeong, J. An","doi":"10.1109/IWW-BCI.2018.8311502","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311502","url":null,"abstract":"Recently, only little is known about the cortical activity of tactile sensation for the dominant and subdominant hand. The objective of this study was to investigate the hemodynamic response of human's cortical brain to tactile sensation to compare the dominant and subdominant hand. Ten healthy adults, 25–35 ages, were enrolled. A 45 channel near-infrared spectroscopy system was used to measure brain responses and a solenoid resonance actuator was utilized to stimulate tactile sensation. The results showed that for the hemodynamic response to both hands on tactile stimulation, the corresponding primary sensory cortex and supplementary motor area were commonly activated, but the tactile stimuli of the subdominant hand induced broader areas of cortical activation than that of the dominant hand. Thus, broad brain areas, including the primary motor cortex and sensory association cortex, were activated by tactile stimulation in subdominant hand. These results suggest that there are differences in brain responses to tactile stimulation of the dominant and subdominant hand, which may reflect the importance of neural adaptability and efficiency for tactile sensation of the hand dominance.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"96 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73834132","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311506
Yoon-Kyu Song, Jihun Lee, Jungwoo Jang, C. Lee, Ah-Hyoung Lee
We have developed a wireless neural recording microimplant for a minimally invasive brain-machine interface. The proposed device utilizes a novel dual-band midfleld antenna to establish an efficient power and data link between an antenna and a coil receiver. It also uses a bipolar junction transistor to convert neural signals into third-order backscattering signals with high detection sensitivity levels. The overall performance of this system is evaluated with a head phantom which closely simulates an in-vivo recording condition. Our antenna achieves high transmission efficiency at 2.5/5 GHz when a miniaturized coil is placed at a target separation distance of about 20mm. This powering scheme allows the neural recording sensor to have a small footprint of a comparable passive neural implant. Thus, we have demonstrated an RFID-like system based on midfield wireless energy/data transfer to extract neural signals from the brain while minimizing potential trauma and physiological interference from the implant.
{"title":"A neural recording microimplants with wireless data and energy transfer link","authors":"Yoon-Kyu Song, Jihun Lee, Jungwoo Jang, C. Lee, Ah-Hyoung Lee","doi":"10.1109/IWW-BCI.2018.8311506","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311506","url":null,"abstract":"We have developed a wireless neural recording microimplant for a minimally invasive brain-machine interface. The proposed device utilizes a novel dual-band midfleld antenna to establish an efficient power and data link between an antenna and a coil receiver. It also uses a bipolar junction transistor to convert neural signals into third-order backscattering signals with high detection sensitivity levels. The overall performance of this system is evaluated with a head phantom which closely simulates an in-vivo recording condition. Our antenna achieves high transmission efficiency at 2.5/5 GHz when a miniaturized coil is placed at a target separation distance of about 20mm. This powering scheme allows the neural recording sensor to have a small footprint of a comparable passive neural implant. Thus, we have demonstrated an RFID-like system based on midfield wireless energy/data transfer to extract neural signals from the brain while minimizing potential trauma and physiological interference from the implant.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"39 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85231875","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311520
Mukhtar M. Alansari, Mahmoud Kamel, B. Hakim, Y. Kadah
To enhance the reliability of motor imagery based brain-computer interface, we present a study that considers subject-based optimization of feature extraction and classification. In particular, wavelet-based feature extraction performed on different bands was optimized over available selections of wavelet family, length and number of decomposition levels. Likewise, the classification step considers three general families of classifiers whose parameters are optimized in a similar manner. Such optimization was performed for each subject whereby processing parameters are selected based on the best performance obtained in the training session. We report the results obtained from applying this approach to the BCI competition 2008 dataset 2b (Graz) and demonstrate that such optimization provides results that outperform previous methods.
{"title":"Study of wavelet-based performance enhancement for motor imagery brain-computer interface","authors":"Mukhtar M. Alansari, Mahmoud Kamel, B. Hakim, Y. Kadah","doi":"10.1109/IWW-BCI.2018.8311520","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311520","url":null,"abstract":"To enhance the reliability of motor imagery based brain-computer interface, we present a study that considers subject-based optimization of feature extraction and classification. In particular, wavelet-based feature extraction performed on different bands was optimized over available selections of wavelet family, length and number of decomposition levels. Likewise, the classification step considers three general families of classifiers whose parameters are optimized in a similar manner. Such optimization was performed for each subject whereby processing parameters are selected based on the best performance obtained in the training session. We report the results obtained from applying this approach to the BCI competition 2008 dataset 2b (Graz) and demonstrate that such optimization provides results that outperform previous methods.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"53 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86564959","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311495
K. Müller
In this short abstract I will discuss recent directions that machine learning and BCI efforts of the BBCI team and coworkers have taken. 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. When analysing Brain Data, it is challenging to combine data streams stemming from various modalities (see e.g. Biessmann et al., 2011, Sui et al., 2012, Fazli et al., 2015, Dähne et al., 2015). Hybrid BCIs are a successful example in this direction (Pfurtscheller et al., 2010, Müller-Putz et al. 2015, Dähne et al. 2015, Fazli et al. 2012, 2015). These techniques are firmly rooted in modern machine learning and signal processing that are now readily in use for analysing EEG, for decoding cognitive states etc. (Nikulin et al. 2007, and see Dornhege et al. 2004, Müller et al. 2008, Bünau et al. 2009, Tomioka and Müller, 2010, Blankertz et al., 2008, 2011, 2016, Lemm et al., 2011, for recent reviews and contributions to Machine Learning for BCI). Note that fusing information has also been a very common practice in the sciences and engineering (W altz and Llinas, 1990). The talk will discuss challenges for BCIs that are to be applied outside controlled lab spaces. Such complex and highly artifactual scenarios demand robust signal processing methods; see e.g. Samek et al. 2014, 2017b for recent reviews on robust methods for BCI. In addition I may expand on technical advances on the explanation framework for deep neural networks (Baehrens et al. 2010, Bach et al. 2015, Lapuschkin et al. 2016a and 2016b, Samek et al. 2017a, Montavon et al. 2017, 2018) to BCI data is given (Sturm et al. 2016). Furthermore, time permitting, I will revisit co-adaptive BCI systems (Vidaurre et al. 2011, Müller et al. 2017) and report on an upcoming study connecting fMRI and EEG data for co-adaptive training (Nierhaus et al. 2017). This abstract is based on joint work with Wojciech Samek, Benjamin Blankertz, Gabriel Curio, Michael Tangermann, Siamac Fazli, Vadim Nikulin, Gregoire Montavon, Sebastian Bach/Lapuschkin, Irene Sturm, Arno Villringer, Carmen Vidaurre, Till Nierhaus and many other members of the Berlin Brain Computer Interface team, the machine learning groups and many more esteemed collaborators. We greatly acknowledge funding by BMBF, EU, DFG and NRF.
{"title":"Towards robust machine learning methods for the analysis of brain data","authors":"K. Müller","doi":"10.1109/IWW-BCI.2018.8311495","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311495","url":null,"abstract":"In this short abstract I will discuss recent directions that machine learning and BCI efforts of the BBCI team and coworkers have taken. 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. When analysing Brain Data, it is challenging to combine data streams stemming from various modalities (see e.g. Biessmann et al., 2011, Sui et al., 2012, Fazli et al., 2015, Dähne et al., 2015). Hybrid BCIs are a successful example in this direction (Pfurtscheller et al., 2010, Müller-Putz et al. 2015, Dähne et al. 2015, Fazli et al. 2012, 2015). These techniques are firmly rooted in modern machine learning and signal processing that are now readily in use for analysing EEG, for decoding cognitive states etc. (Nikulin et al. 2007, and see Dornhege et al. 2004, Müller et al. 2008, Bünau et al. 2009, Tomioka and Müller, 2010, Blankertz et al., 2008, 2011, 2016, Lemm et al., 2011, for recent reviews and contributions to Machine Learning for BCI). Note that fusing information has also been a very common practice in the sciences and engineering (W altz and Llinas, 1990). The talk will discuss challenges for BCIs that are to be applied outside controlled lab spaces. Such complex and highly artifactual scenarios demand robust signal processing methods; see e.g. Samek et al. 2014, 2017b for recent reviews on robust methods for BCI. In addition I may expand on technical advances on the explanation framework for deep neural networks (Baehrens et al. 2010, Bach et al. 2015, Lapuschkin et al. 2016a and 2016b, Samek et al. 2017a, Montavon et al. 2017, 2018) to BCI data is given (Sturm et al. 2016). Furthermore, time permitting, I will revisit co-adaptive BCI systems (Vidaurre et al. 2011, Müller et al. 2017) and report on an upcoming study connecting fMRI and EEG data for co-adaptive training (Nierhaus et al. 2017). This abstract is based on joint work with Wojciech Samek, Benjamin Blankertz, Gabriel Curio, Michael Tangermann, Siamac Fazli, Vadim Nikulin, Gregoire Montavon, Sebastian Bach/Lapuschkin, Irene Sturm, Arno Villringer, Carmen Vidaurre, Till Nierhaus and many other members of the Berlin Brain Computer Interface team, the machine learning groups and many more esteemed collaborators. We greatly acknowledge funding by BMBF, EU, DFG and NRF.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"13 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79647209","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311503
A. Nurmikko
The development of techniques for reading information from the brain to translate e.g. movement intentions to control of robotic hands and operating simple tablet-base communication devices by tetraplegic devices is an example of a contemporary BCI operating at a system level of neuro-technology innovation. At the same time, the BCI field could be viewed still at infancy, with both challenges and opportunities for development of considerably more advanced BCIs. For example, the physical cabling of neural sensors such as microelectrode arrays to external electronics is now witnessing a transition to wireless sensors thereby enabling higher degree of mobility of subjects.
{"title":"Approaches to large scale neural recording by chronic implants for mobile BCIs","authors":"A. Nurmikko","doi":"10.1109/IWW-BCI.2018.8311503","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311503","url":null,"abstract":"The development of techniques for reading information from the brain to translate e.g. movement intentions to control of robotic hands and operating simple tablet-base communication devices by tetraplegic devices is an example of a contemporary BCI operating at a system level of neuro-technology innovation. At the same time, the BCI field could be viewed still at infancy, with both challenges and opportunities for development of considerably more advanced BCIs. For example, the physical cabling of neural sensors such as microelectrode arrays to external electronics is now witnessing a transition to wireless sensors thereby enabling higher degree of mobility of subjects.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"23 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80932701","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311500
M. Lebedev
While brain-machine interfaces (BMIs) strive to provide neural prosthetic solutions to people with sensory, motor and cognitive disabilities, they have been typically tested in strictly controlled laboratory settings. Making BMIs versatile and applicable to real life situations is a significant challenge. For example, in real life we can flexibly and independently control multiple behavioral variables, such as programming motor goals, orienting attention in space, fixating objects with the eyes, and remembering relevant information. Several neurophysiological experiments, conducted in monkeys, manipulated multiple behavioral variables in a controlled way; these multiple variables were decoded from the activity of same neuronal ensembles. Additionally, in the other monkey experiments, multiple motor variables were extracted from cortical ensembles in real time, such as controlling two virtual arms using a BMI. The next improvement has been achieved using brain-machine-brain interfaces (BMBIs) that simultaneously extract motor intentions from brain activity and generate artificial sensations using intracortical microstimulation (ICMS). For example, a BMBI can perform active tactile exploration of virtual objects. Such versatile BMIs bring us closer to the development of clinical neural prostheses for restoration and rehabilitation of neural function.
{"title":"Towards a versatile brain-machine interface: Neural decoding of multiple behavioral variables and delivering sensory feedback versatile brain-machine interface","authors":"M. Lebedev","doi":"10.1109/IWW-BCI.2018.8311500","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311500","url":null,"abstract":"While brain-machine interfaces (BMIs) strive to provide neural prosthetic solutions to people with sensory, motor and cognitive disabilities, they have been typically tested in strictly controlled laboratory settings. Making BMIs versatile and applicable to real life situations is a significant challenge. For example, in real life we can flexibly and independently control multiple behavioral variables, such as programming motor goals, orienting attention in space, fixating objects with the eyes, and remembering relevant information. Several neurophysiological experiments, conducted in monkeys, manipulated multiple behavioral variables in a controlled way; these multiple variables were decoded from the activity of same neuronal ensembles. Additionally, in the other monkey experiments, multiple motor variables were extracted from cortical ensembles in real time, such as controlling two virtual arms using a BMI. The next improvement has been achieved using brain-machine-brain interfaces (BMBIs) that simultaneously extract motor intentions from brain activity and generate artificial sensations using intracortical microstimulation (ICMS). For example, a BMBI can perform active tactile exploration of virtual objects. Such versatile BMIs bring us closer to the development of clinical neural prostheses for restoration and rehabilitation of neural function.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"73 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74675404","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311522
Dongjae Kim, C. Weston, Sang Wan Lee
Human reinforcement learning (RL) has been known to utilize two distinctive learning strategies, model-based (MB) and model-free (MF) RL. The process of arbitration between MB and MF is thought to be located in the ventrolateral prefrontal cortex and frontopolar cortex. These loci are near the cortex, so we expect the related information can be represented in EEG signals. However, EEG signal patterns considering the arbitration of RL has not been investigated. In this paper, we tested a EEG-based classification model to separate these two different types of trials, each of which is meant to promote MB and MF RL. We found, for the first time, firm evidence to indicate that information pertaining to learning strategies is represented in prefrontal EEG signals.
{"title":"EEG-based classification of learning strategies : Model-based and model-free reinforcement learning","authors":"Dongjae Kim, C. Weston, Sang Wan Lee","doi":"10.1109/IWW-BCI.2018.8311522","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311522","url":null,"abstract":"Human reinforcement learning (RL) has been known to utilize two distinctive learning strategies, model-based (MB) and model-free (MF) RL. The process of arbitration between MB and MF is thought to be located in the ventrolateral prefrontal cortex and frontopolar cortex. These loci are near the cortex, so we expect the related information can be represented in EEG signals. However, EEG signal patterns considering the arbitration of RL has not been investigated. In this paper, we tested a EEG-based classification model to separate these two different types of trials, each of which is meant to promote MB and MF RL. We found, for the first time, firm evidence to indicate that information pertaining to learning strategies is represented in prefrontal EEG signals.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"26 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78991668","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311497
Vinicio Changoluisa, P. Varona, F. B. Rodríguez
Brain Computer Interface (BCI) technologies use neural activity to implement a direct communication channel for healthy and disable subjects. To achieve this, many investigations look to improve BCI precision by increasing the number of electrodes with standard configurations, ignoring inter- and intra-subject variability. To control this variability in event-related potential (ERP)-based BCIs we propose to investigate the cumulative peak difference, an intrinsic characteristic of ERP, as a measure for electrode selection. The results shown in this work indicate that the proposed method improved accuracy and bitrate in all analyzed electrode sets. Our work contributes to the management of inter- and intra-subject variability which helps to design accurate and low-cost BCIs.
{"title":"An electrode selection approach in P300-based BCIs to address inter- and intra-subject variability","authors":"Vinicio Changoluisa, P. Varona, F. B. Rodríguez","doi":"10.1109/IWW-BCI.2018.8311497","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311497","url":null,"abstract":"Brain Computer Interface (BCI) technologies use neural activity to implement a direct communication channel for healthy and disable subjects. To achieve this, many investigations look to improve BCI precision by increasing the number of electrodes with standard configurations, ignoring inter- and intra-subject variability. To control this variability in event-related potential (ERP)-based BCIs we propose to investigate the cumulative peak difference, an intrinsic characteristic of ERP, as a measure for electrode selection. The results shown in this work indicate that the proposed method improved accuracy and bitrate in all analyzed electrode sets. Our work contributes to the management of inter- and intra-subject variability which helps to design accurate and low-cost BCIs.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"424 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76743284","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311532
Junhyuk Choi, S. Lee, Seung-jong Kim, Jong Min Lee, Hyungmin Kim
In this study, we employed a linear classifier to grasp the abstract features of electroencephalography (EEG) for recognizing voluntary gait intention and termination. We monitored Mu-band EEG to find gait intention and tried to detect a movement on/offset. Considerable gait-related (de) synchronization occurred hence, amplified by common spatial pattern (CSP). Performance of the classifier was evaluated in terms of classification success rates and false positive rates.
{"title":"Detecting voluntary gait initiation/termination intention using EEG","authors":"Junhyuk Choi, S. Lee, Seung-jong Kim, Jong Min Lee, Hyungmin Kim","doi":"10.1109/IWW-BCI.2018.8311532","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311532","url":null,"abstract":"In this study, we employed a linear classifier to grasp the abstract features of electroencephalography (EEG) for recognizing voluntary gait intention and termination. We monitored Mu-band EEG to find gait intention and tried to detect a movement on/offset. Considerable gait-related (de) synchronization occurred hence, amplified by common spatial pattern (CSP). Performance of the classifier was evaluated in terms of classification success rates and false positive rates.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"9 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88175798","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 : 2018-01-01DOI: 10.1109/IWW-BCI.2018.8311511
Inhwa Han, Sang-Hwa Won, Yungeui Kang, Kyungseok Oh, Kye-Yoep Kim, Janghwan Jekal, S. Jin, Gihyoun Lee, Seung Tae Yang, Seon Yoon Jung, J. An
Until recently, pain assessment has largely relied on subjective self-reports such as questionnaires or VAS. This paper attempts to objectively quantify pain from a neurological point of view through the characteristics of cerebral hemodynamics. Functional near-infrared spectroscopy (fNIRS) measures cortical blood flow changes during electrical pain stimulation. The selected feature of pain measure is the concentration change of oxygenated hemoglobin. Cortical activation patterns and time-series analysis for region of interest shows that premotor cortex and primary motor cortex as well as somatosensory cortex are involved in pain perception. These results are consistent with the findings of fMRI studies on physical pain. Oxygenated hemoglobin is therefore likely to be a quantitative biomarker of pain.
{"title":"Cortical activation patterns of electrical pain stimulation using fNIRS","authors":"Inhwa Han, Sang-Hwa Won, Yungeui Kang, Kyungseok Oh, Kye-Yoep Kim, Janghwan Jekal, S. Jin, Gihyoun Lee, Seung Tae Yang, Seon Yoon Jung, J. An","doi":"10.1109/IWW-BCI.2018.8311511","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2018.8311511","url":null,"abstract":"Until recently, pain assessment has largely relied on subjective self-reports such as questionnaires or VAS. This paper attempts to objectively quantify pain from a neurological point of view through the characteristics of cerebral hemodynamics. Functional near-infrared spectroscopy (fNIRS) measures cortical blood flow changes during electrical pain stimulation. The selected feature of pain measure is the concentration change of oxygenated hemoglobin. Cortical activation patterns and time-series analysis for region of interest shows that premotor cortex and primary motor cortex as well as somatosensory cortex are involved in pain perception. These results are consistent with the findings of fMRI studies on physical pain. Oxygenated hemoglobin is therefore likely to be a quantitative biomarker of pain.","PeriodicalId":6537,"journal":{"name":"2018 6th International Conference on Brain-Computer Interface (BCI)","volume":"2028 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91318528","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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