Pub Date : 2018-10-17DOI: 10.5772/INTECHOPEN.75522
I. Weinberg, L. Mair, S. Jafari, J. Algarin, J. B. Baviera, J. Baker-McKee, Bradley English, SagarChowdhury, Pulkit Malik, J. Watson-Daniels, Olivia Hale, P. Stepanov, A. Nacev, R. Hilaman, Said Ijanaten, Christian Koudelka, R. Araneda, J. Herberholz, L. Martínez-Miranda, B. Shapiro, P. S. Villar, I. Krivorotov, S. Khizroev, S. Fricke
We are developing methods of noninvasively delivering magnetic neuroparticles™ via intranasal administration followed by image-guided magnetic propulsion to selected locations in the brain. Once placed, the particles can activate neurons via vibrational motion or magnetoelectric stimulation. Similar particles might be used to read out neuronal electrical pulses via spintronic or liquid-crystal magnetic interactions, for fast bidirec- tional brain-machine interface. We have shown that particles containing liquid crystals can be read out with magnetic resonance imaging (MRI) using embedded magnetic nanoparticles and that the signal is visible even for voltages comparable to physiological characteristics. Such particles can be moved within the brain (e.g., across midline) with- out causing changes to neurological firing.
{"title":"Image-guided Placement of Magnetic Neuroparticles as a Potential High-Resolution Brain-Machine Interface","authors":"I. Weinberg, L. Mair, S. Jafari, J. Algarin, J. B. Baviera, J. Baker-McKee, Bradley English, SagarChowdhury, Pulkit Malik, J. Watson-Daniels, Olivia Hale, P. Stepanov, A. Nacev, R. Hilaman, Said Ijanaten, Christian Koudelka, R. Araneda, J. Herberholz, L. Martínez-Miranda, B. Shapiro, P. S. Villar, I. Krivorotov, S. Khizroev, S. Fricke","doi":"10.5772/INTECHOPEN.75522","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75522","url":null,"abstract":"We are developing methods of noninvasively delivering magnetic neuroparticles™ via intranasal administration followed by image-guided magnetic propulsion to selected locations in the brain. Once placed, the particles can activate neurons via vibrational motion or magnetoelectric stimulation. Similar particles might be used to read out neuronal electrical pulses via spintronic or liquid-crystal magnetic interactions, for fast bidirec- tional brain-machine interface. We have shown that particles containing liquid crystals can be read out with magnetic resonance imaging (MRI) using embedded magnetic nanoparticles and that the signal is visible even for voltages comparable to physiological characteristics. Such particles can be moved within the brain (e.g., across midline) with- out causing changes to neurological firing.","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128329586","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 : 2018-10-17DOI: 10.5772/INTECHOPEN.75132
Bijay Guragain, Ali Haider, R. Fazel-Rezai
Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient per - formance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly dis - cussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented. ] merg-ing ERD to control a device such that additional features of one could be used to another. are The common hybrid systems based on signal combinations as well as operation methods, their performances, and improvements are Statistical analysis of BCI and hybrid BCI to P300 and SSVEP are on publications. Transitioning from laboratory to the possible commercial applications with the limi tations This P300, SSVEP, and MI which used EEG sig nals for BCI. Simultaneous operation is very common in P300-SSVEP hybrid and sequential are incorporated in MI-related hybrid experiments. Average accuracy ITR among
{"title":"Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends","authors":"Bijay Guragain, Ali Haider, R. Fazel-Rezai","doi":"10.5772/INTECHOPEN.75132","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75132","url":null,"abstract":"Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient per - formance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly dis - cussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented. ] merg-ing ERD to control a device such that additional features of one could be used to another. are The common hybrid systems based on signal combinations as well as operation methods, their performances, and improvements are Statistical analysis of BCI and hybrid BCI to P300 and SSVEP are on publications. Transitioning from laboratory to the possible commercial applications with the limi tations This P300, SSVEP, and MI which used EEG sig nals for BCI. Simultaneous operation is very common in P300-SSVEP hybrid and sequential are incorporated in MI-related hybrid experiments. Average accuracy ITR among","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134271440","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 : 2018-10-17DOI: 10.5772/INTECHOPEN.78695
M. Alimardani, S. Nishio, H. Ishiguro
Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.
{"title":"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment","authors":"M. Alimardani, S. Nishio, H. Ishiguro","doi":"10.5772/INTECHOPEN.78695","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.78695","url":null,"abstract":"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121879135","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 : 2018-10-17DOI: 10.5772/INTECHOPEN.75693
R. Singla
This chapter describes the method of flickering targets, eliciting fundamental frequency changes in the EEG signal of the subject, used to drive machine commands after interpretation of user’s intentions. The steady-state response of the changes in the EEG caused by events such as visual stimulus applied to the subject via a computer screen is called steady-state visually evoked potential (SSVEP). This feature of the EEG signal can be used to form a basis of input to assistive devices for locked in patients to improve their quality of life, as well as for performance enhancing devices for healthy subjects. The contents of this chapter describe the SSVEP stimuli; feature extraction techniques, feature classification techniques and a few applications based on SSVEP based BCI.
{"title":"SSVEP-Based BCIs","authors":"R. Singla","doi":"10.5772/INTECHOPEN.75693","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75693","url":null,"abstract":"This chapter describes the method of flickering targets, eliciting fundamental frequency changes in the EEG signal of the subject, used to drive machine commands after interpretation of user’s intentions. The steady-state response of the changes in the EEG caused by events such as visual stimulus applied to the subject via a computer screen is called steady-state visually evoked potential (SSVEP). This feature of the EEG signal can be used to form a basis of input to assistive devices for locked in patients to improve their quality of life, as well as for performance enhancing devices for healthy subjects. The contents of this chapter describe the SSVEP stimuli; feature extraction techniques, feature classification techniques and a few applications based on SSVEP based BCI.","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127395794","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 : 2018-10-17DOI: 10.5772/INTECHOPEN.76332
Haitham Hasan Badi, Bassam Sabbri, Fitian Ajeel
Nowadays, image forgery has become common because only an editing package soft- ware and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applica- tions and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group com -pression. This study proposes an algorithm that detects image falsification on the basis of Hessian features. automatic reinstallation of duplicate areas hinders the practical applications of these algorithms. We propose a novel algorithm for the detection and description of scale and constant rotation in images. The algorithm is based on SURF and thus has powerful accel eration functions. SURF approximates or even exceeds the proposed thresholds for redun -dancy, excellence, and sustainability and rapidly performs calculation and comparison. This operation is performed by relying on image confluence. The exit detection and prescriptive prescriptions are based on their strengths (if a Hessian scale is used to detect and describe the established distribution), and kernel methods are simplified to allow the combination of new detection, description, and correspondence. Correspondence between two images of the same view and the objective is partly achieved by using many computers. In this study, pho -tography, three-dimensional reconstruction, image recording, and objective recoding were conducted. The search for a separate image match—the purpose of our research—can be separated into three principal steps. First, points of interest are specified in the characteristic locations of the image, such as angles, points, and plus T-intersections. The most important property of a detection method is its repeatability, that is, its reliability in finding similar indicators of interest under different conditions. Then, each point of interest is represented by a transmitter characteristic. This description must be distinct and must have similar time strengths under noise conditions, mistake detection, and geometrical and photometrical distortions. Finally, vector descriptors are adapted in different images. Correspondence is based on vector distance. Descriptor size directly affects computational time. Thus, fewer dimensions are desired. We aimed to develop an algorithm for the detection and the iden tification of fraud. We compared the performance of our proposed algorithm with that of a state-of-the-art detection algorithm. Our algorithm exhibits computational time and robust performance. Downsizing after description and complexity must be balanced while provid ing sufficie
{"title":"Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery","authors":"Haitham Hasan Badi, Bassam Sabbri, Fitian Ajeel","doi":"10.5772/INTECHOPEN.76332","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.76332","url":null,"abstract":"Nowadays, image forgery has become common because only an editing package soft- ware and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applica- tions and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group com -pression. This study proposes an algorithm that detects image falsification on the basis of Hessian features. automatic reinstallation of duplicate areas hinders the practical applications of these algorithms. We propose a novel algorithm for the detection and description of scale and constant rotation in images. The algorithm is based on SURF and thus has powerful accel eration functions. SURF approximates or even exceeds the proposed thresholds for redun -dancy, excellence, and sustainability and rapidly performs calculation and comparison. This operation is performed by relying on image confluence. The exit detection and prescriptive prescriptions are based on their strengths (if a Hessian scale is used to detect and describe the established distribution), and kernel methods are simplified to allow the combination of new detection, description, and correspondence. Correspondence between two images of the same view and the objective is partly achieved by using many computers. In this study, pho -tography, three-dimensional reconstruction, image recording, and objective recoding were conducted. The search for a separate image match—the purpose of our research—can be separated into three principal steps. First, points of interest are specified in the characteristic locations of the image, such as angles, points, and plus T-intersections. The most important property of a detection method is its repeatability, that is, its reliability in finding similar indicators of interest under different conditions. Then, each point of interest is represented by a transmitter characteristic. This description must be distinct and must have similar time strengths under noise conditions, mistake detection, and geometrical and photometrical distortions. Finally, vector descriptors are adapted in different images. Correspondence is based on vector distance. Descriptor size directly affects computational time. Thus, fewer dimensions are desired. We aimed to develop an algorithm for the detection and the iden tification of fraud. We compared the performance of our proposed algorithm with that of a state-of-the-art detection algorithm. Our algorithm exhibits computational time and robust performance. Downsizing after description and complexity must be balanced while provid ing sufficie","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130112258","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 : 2018-10-17DOI: 10.5772/INTECHOPEN.75009
Jzau-Sheng Lin, Ray Shihb
Motor imagery brain-computer interface (BCI) by using of deep-learning models is pro- posed in this paper. In which, we used the electroencephalogram (EEG) signals of motor imagery (MI-EEG) to identify different imagery activities. The brain dynamics of motor imagery are usually measured by EEG as non-stationary time series of low signal-to-noise ratio. However, a variety of methods have been previously developed to classify MI-EEG signals getting not satisfactory results owing to lack of characteristics in time-frequency features. In this paper, discrete wavelet transform (DWT) was applied to transform MIEEG signals and extract their effective coefficients as the time-frequency features. Then two deep learning (DL) models named Long-short term memory (LSTM) and gated recurrent neu- ral networks (GRNN) are used to classify MI-EEG data. LSTM is designed to fight against vanishing gradients. GRNN makes each recurrent unit to capture dependencies of differ - ent time scales adaptively. Similar scheme of the LSTM unit, GRNN has gating units that modulate the flow of information inside the unit, but without having a separate memory cells. Experimental results show that GRNN and LSTM yield higher classification accura-cies compared to the existing approaches that is helpful for the further research and applica- tion of relative RNN in processing of MI-EEG.
{"title":"A Motor-Imagery BCI System Based on Deep Learning Networks and Its Applications","authors":"Jzau-Sheng Lin, Ray Shihb","doi":"10.5772/INTECHOPEN.75009","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75009","url":null,"abstract":"Motor imagery brain-computer interface (BCI) by using of deep-learning models is pro- posed in this paper. In which, we used the electroencephalogram (EEG) signals of motor imagery (MI-EEG) to identify different imagery activities. The brain dynamics of motor imagery are usually measured by EEG as non-stationary time series of low signal-to-noise ratio. However, a variety of methods have been previously developed to classify MI-EEG signals getting not satisfactory results owing to lack of characteristics in time-frequency features. In this paper, discrete wavelet transform (DWT) was applied to transform MIEEG signals and extract their effective coefficients as the time-frequency features. Then two deep learning (DL) models named Long-short term memory (LSTM) and gated recurrent neu- ral networks (GRNN) are used to classify MI-EEG data. LSTM is designed to fight against vanishing gradients. GRNN makes each recurrent unit to capture dependencies of differ - ent time scales adaptively. Similar scheme of the LSTM unit, GRNN has gating units that modulate the flow of information inside the unit, but without having a separate memory cells. Experimental results show that GRNN and LSTM yield higher classification accura-cies compared to the existing approaches that is helpful for the further research and applica- tion of relative RNN in processing of MI-EEG.","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130763053","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 : 2018-03-13DOI: 10.5772/INTECHOPEN.75411
Y. Bunno
We investigated the influence of the imagined muscle contraction strength on the spinal motor neural excitability and sympathetic nerve activity by using the F-wave and heart rate variability analysis. Motor imagery of isometric thenar muscle activity increased the spinal motor neuron excitability and sympathetic nerve activity. The imagined muscle contraction strength did not affect changes of the spinal motor neuron excitability and sympathetic nerve activity. Therefore, Motor imagery at slight imagined muscle contraction strength can facilitate the spinal motor neuron excitability without physical load. Motor imagery-based Brain-machine interface is widely used for neurorehabilitation. To achieve better outcomes in neurorehabilitation used Brain-machine interface, performing trained motor imagery would be required, and the F-wave may be exploited an index of motor imagery training effect.
{"title":"The Application of Motor Imagery to Neurorehabilitation","authors":"Y. Bunno","doi":"10.5772/INTECHOPEN.75411","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.75411","url":null,"abstract":"We investigated the influence of the imagined muscle contraction strength on the spinal motor neural excitability and sympathetic nerve activity by using the F-wave and heart rate variability analysis. Motor imagery of isometric thenar muscle activity increased the spinal motor neuron excitability and sympathetic nerve activity. The imagined muscle contraction strength did not affect changes of the spinal motor neuron excitability and sympathetic nerve activity. Therefore, Motor imagery at slight imagined muscle contraction strength can facilitate the spinal motor neuron excitability without physical load. Motor imagery-based Brain-machine interface is widely used for neurorehabilitation. To achieve better outcomes in neurorehabilitation used Brain-machine interface, performing trained motor imagery would be required, and the F-wave may be exploited an index of motor imagery training effect.","PeriodicalId":448864,"journal":{"name":"Evolving BCI Therapy - Engaging Brain State Dynamics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133532383","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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