2016 4th International Winter Conference on Brain-Computer Interface (BCI)最新文献

{"title":"Decoding details of human functions using electrocorticography","authors":"G. Schalk","doi":"10.1109/IWW-BCI.2016.7457444","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457444","url":null,"abstract":"Our laboratory integrates and advances scientific, engineering, and clinical concepts to innovate, develop and test new neurotechnologies and to apply them to basic and applied research. These multidisciplinary efforts span a variety of areas, including computational, cognitive, and systems neuroscience, signal processing, machine learning, statistics, computer science, and neurology/neurosurgery. Our vision is to revolutionize the way we can study the brain, and to develop important clinical tools for diagnosis or treatment of nervous system function.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132508359","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}

{"title":"Machine learning for BCI: towards analysing cognition","authors":"K. Müller","doi":"10.1109/IWW-BCI.2016.7457453","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457453","url":null,"abstract":"This article discusses machine learning and BCI with a focus on analysing cognition, a topic that has been extensively covered by the author and co-workers in numerous papers and conference papers. Due to the review character of the presentation, a high overlap with the above-mentioned contributions is unavoidable. When analysing cognition, it is often useful to combine information from various modalities (see e.g. Biessmann et al., 2011, Sui et al., 2012). In BCI recently multimodal fusion concepts have received great attention under the label hybrid BCI (Pfurtscheller et al., 2010, Müller-Putz et al. 2015, Dähne et al. 2015, Fazli et al. 2015) or as data analysis technique for extracting (non-) linear relations between data (see e.g. Biessmann et al., 2010, Biessmann et al., 2011, Fazli et al., 2009, 2011, 2012, Dähne et al., 2013, 2014a,b, 2015, Winkler et al. 2015). They are rooted in the modern machine learning and signal processing techniques that are now available for analysing EEG, for decoding mental states etc. (see Müller et al. 2008, Bünau et al. 2009, Tomioka and Müller, 2010, Blankertz et al., 2008, 2011, Lemm et al., 2011, Porbadnigk et al. 2015 for recent reviews and contributions to Machine Learning for BCI, see Samek et al. 2014 for a review on robust methods). Note that fusing information has also been a very common practice in the sciences and engineering (Waltz and Llinas, 1990). The talk will discuss a number of recent contributions from the BBCI group that have helped to broaden the spectrum of applicability for Brain Computer Interfaces and mental state monitoring in particular and for analysis of neuroimaging data in general. I will introduce a novel reliable method for estimating the Hurst exponent, a quantity that has recently become popular for describing network properties and is being used for diagnostic purposes (cf. Blythe et al. 2014). It is applied to estimate and analyse cognitive properties in neurophysiological data from BCI experiments (Samek et al. 2016). Furthermore if time permits I will discuss a recent attractive application of BCI in the context of video coding (Scholler et al. 2012 and Acqualagna et al 2015).","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123582799","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}

{"title":"Common neural mechanism for reaching movements","authors":"H. Yeom, J. Kim, C. Chung","doi":"10.1109/IWW-BCI.2016.7457439","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457439","url":null,"abstract":"The mechanism of skilled movements has been considered to differ from the rhythmic movement. However, recent studies shows that the spinal cord may also be involved in the generation of skilled movements. Direct and indirect pathways connect between broad motor-related cortex and spinal cord. Therefore, if the oscillations are generated in the spinal cord like rhythmic movements and the oscillation interact with the broad motor related cortex, the oscillatory components should be found in macroscopic neural activity. Here, we examine whether the oscillations are represented in macroscopic neural activity during skilled movements. To investigate neural activity, we measured whole brain MEG signals during reaching. We used a novel analysis method, `jPCA', to analyze the MEG signals. We found that neural oscillations occur at the macroscopic level in all subjects during reaching movements. The results imply the possibility that the corticospinal system is involved in the generation and control of the skilled movements. Our results suggest that the neural mechanism of skilled movements is similar to the mechanism of rhythmic movements.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122075778","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}

{"title":"Domain knowledge and feature representation","authors":"Mark S. Cohen","doi":"10.1109/IWW-BCI.2016.7457438","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457438","url":null,"abstract":"Identifying covert internal brain by their expression in neural images, particularly from magnetic resonance imaging, is a popular, powerful, and important area of research whose ultimate expression is known now as “brain reading.” The nature of the imaging data is challenging however, in that they typically have two orders of magnitude more features than observations. We propose that it is many ways useful to apply prior knowledge of brain organization - both physical and mental. We note that sparsity offers quantitative leverage, and that this, itself, may provide insight into the nature of human cognition.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131047876","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}

{"title":"Hand motion reconstruction using EEG and EMG","authors":"Jacobo Fernández-Vargas, T. Tarvainen, K. Kita, Wenwei Yu","doi":"10.1109/IWW-BCI.2016.7457457","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457457","url":null,"abstract":"Motion reconstruction of continuous hand movement is a problem that can be solved in different ways. Using invasive technologies has showed great results. However obtaining similar precision with non-invasive methods is something that has not been achieved. In particular we focus our attention on prosthetic devices for trans-humeral amputees. In this study we use two different non-invasive acquisition systems (EEG and EMG) in combination with two different predictor architectures to find the most appropriate to solve the problem. In addition, the importance of each one of the systems was studied along with the importance of the previously reconstructed positions. Data were collected from 16 healthy subjects, reaching correlation values up to 0.893.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"417 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126945216","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}

{"title":"Development of an open source platform for brain-machine interface: openBMI","authors":"Min-Ho Lee, S. Fazli, Keun-Tae Kim, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2016.7457440","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457440","url":null,"abstract":"Recently, there has been an increased demand for Brain-Machine Interface (BMI) toolboxes for neuroscience research. A BMI system provides a communication tool for people with severe motor disabilities and can be used to control external devices. Here, we introduce an open-source BMI platform, named `OpenBMI'. OpenBMI offers various BMI paradigms, signal processing tools, data analysis techniques as well as real-time feedback modules. The OpenBMI toolbox covers the entire processing chains for offline and online analysis of common BMI paradigms, such as motor-imagery (MI), event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEP). In this article, the OpenBMI framework, its features as well as its future development plan is introduced.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614540","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}

{"title":"Interfacing brain and computer in neurorehabilitation","authors":"D. Mattia, L. Astolfi, J. Toppi, M. Petti, F. Pichiorri, F. Cincotti","doi":"10.1109/IWW-BCI.2016.7457446","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457446","url":null,"abstract":"Brain-Computer Interface (BCI) technology exploiting electroencephalographic (EEG) sensorimotor oscillations is a potential tool to promote neuronal plasticity after stroke and ultimately to improve motor rehabilitation outcomes in post-stroke sub-acute and chronic stage. Nevertheless, the cost/effectiveness of BCI-based rehabilitative interventions remains to be addressed when considering the heterogeneity in the post-stroke functional motor impairment (clinical status) and the central nervous system reorganization (neuroplasticity). In this paper, we will provide preliminary findings on how multimodal neurophysiological methods to asses potential recovery after stroke can be use to foster an effective applicability of BCI technology in neuro-rehabilitation care.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116294287","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}

{"title":"Dimensionality reduction with isomap algorithm for EEG covariance matrices","authors":"Egor Krivov, M. Belyaev","doi":"10.1109/IWW-BCI.2016.7457448","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457448","url":null,"abstract":"This paper presents new approach to braincomputer interface construction. Most algorithms for EEG classification use spatial covariance matrices, that contain information about synchronisation and desynchronisation in human brain. Suggested algorithm involves Riemannian geometry in the space of symmetric and positive-definite matrices to measure distances between covariance matrices in more accurate fashion. Then Isomap algorithm is applied to the Riemannian pairwise distances to locate manifold, corresponding to human EEG signals, and arrange points, corresponding to covariance matrices, in low-dimensional space, preserving geodesical distances. Finally, linear discriminant analysis is applied for classification. Suggested algorithm is tested on Berlin BCI dataset and compared with state-of-the-art algorithms common spatial patterns and classification in tangent space.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114799136","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}

{"title":"P300-BCI-based authentication system","authors":"Moonwon Yu, Netiwit Kaongoen, Sungho Jo","doi":"10.1109/IWW-BCI.2016.7457443","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457443","url":null,"abstract":"An authentication system is the system that decides whether to accept or reject the claiming identity of a person. Biometric-based authentication utilizes the individuality in human physiological and behavioral characteristics to authorize a person. Brain-signal-based authentication system is relatively new comparing to other types of biometric data. In this paper, we proposed a novel method that applies P300-based Brain Computer Interface (BCI) technique to the authentication system. The main concept for P300-BCI-based authentication is that the Oddball paradigm eliciting P300 waves is secret to the attacker. The experiments were conducted to evaluate the proposed system. The trained P300 classification model has 0.831 accuracy rate. And the proposed authentication system has 0.325 False Rejection Rate (FRR), 0.00 False Acceptation Rate (FAR) for secret-unknown attack and 0.10 FAR for secret-known attack. This study has shown that P300 wave has good potential as a biometric for highly secured authentication system.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133837809","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}

{"title":"Finger flexion imagery: EEG classification through physiologically-inspired feature extraction and hierarchical voting","authors":"Daniel Furman, Roi Reichart, H. Pratt","doi":"10.1109/IWW-BCI.2016.7457445","DOIUrl":"https://doi.org/10.1109/IWW-BCI.2016.7457445","url":null,"abstract":"Accurate electroencephalography (EEG) classification of finger flexion imagery would endow non-invasive brainmachine interfaces (BMIs) with a much richer control repertoire. Traditionally, it has been assumed that non-invasive methods lack the resolution required for success on such a fine discrimination task. In this study, we challenged this assumption. EEG was acquired while subjects imagined performing individual and bimanual finger flexions. A new method of spatiotemporal and spectral feature extraction was applied, and multi-class support vector machine (SVM) classifiers were trained. Predictions and probabilities then served as inputs to a novel voting scheme, which output the system decision. The present approach achieved a mean population (n=15) accuracy of 30.86±1.76%, nearly twice the chance guessing level (16.71±1.68%) for the six-class task evaluated. Finger imagery is thus shown to be classifiable through EEG analysis alone.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"11 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123310901","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}