@article {3406, title = {ECoG high gamma activity reveals distinct cortical representations of lyrics passages, harmonic and timbre-related changes in a rock song.}, journal = {Front Hum Neurosci}, volume = {8}, year = {2014}, month = {10/2014}, pages = {798}, abstract = {

Listening to music moves our minds and moods, stirring interest in its neural underpinnings. A multitude of compositional features drives the appeal of natural music. How such original music, where a composer{\textquoteright}s opus is not manipulated for experimental purposes, engages a listener{\textquoteright}s brain has not been studied until recently. Here, we report an in-depth analysis of two electrocorticographic (ECoG) data sets obtained over the left hemisphere in ten patients during presentation of either a rock song or a read-out narrative. First, the time courses of five acoustic features (intensity, presence/absence of vocals with lyrics, spectral centroid, harmonic change, and pulse clarity) were extracted from the audio tracks and found to be correlated with each other to varying degrees. In a second step, we uncovered the specific impact of each musical feature on ECoG high-gamma power (70-170 Hz) by calculating partial correlations to remove the influence of the other four features. In the music condition, the onset and offset of vocal lyrics in ongoing instrumental music was consistently identified within the group as the dominant driver for ECoG high-gamma power changes over temporal auditory areas, while concurrently subject-individual activation spots were identified for sound intensity, timbral, and harmonic features. The distinct cortical activations to vocal speech-related content embedded in instrumental music directly demonstrate that song integrated in instrumental music represents a distinct dimension in complex music. In contrast, in the speech condition, the full sound envelope was reflected in the high gamma response rather than the onset or offset of the vocal lyrics. This demonstrates how the contributions of stimulus features that modulate the brain response differ across the two examples of a full-length natural stimulus, which suggests a context-dependent feature selection in the processing of complex auditory stimuli.

}, keywords = {acoustic features, electrocorticography (ECoG), high gamma, music processing, natural music}, issn = {1662-5161}, doi = {10.3389/fnhum.2014.00798}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25352799}, author = {Sturm, Irene and Benjamin Blankertz and Potes, Cristhian and Gerwin Schalk and Curio, Gabriel} } @inbook {2864, title = {BCI Software Platforms.}, booktitle = {Towards Practical Brain-Computer Interfaces}, year = {2013}, publisher = {Biological and Medical Physics}, organization = {Biological and Medical Physics}, abstract = {In this chapter, we provide an overview of publicly available software platforms for brain{\textendash}computer interfaces. We have identified seven major BCI platforms and one platform specifically targeted towards feedback and stimulus presentation. We describe the intended target user group (which includes researchers, programmers, and end users), the most important features of each platform such as availability on different operating systems, licences, programming languages involved, supported devices, and so on. These seven platforms are: (1) BCI2000, (2) OpenViBE, (3) TOBI Common Implementation Platform (CIP), (4) BCILAB, (5) BCI++, (6) xBCI, and (7) BF++. The feedback framework is called Pyff. Our conclusion discusses possible synergies and future developments, such as combining different components of different platforms. With this overview, we hope to identify the strengths and weaknesses of each available platform, which should help anyone in the BCI research field in their decision which platform to use for their specific purposes.}, isbn = {978-3-642-29745-8}, doi = {DOI: 10.1007/978-3-642-29746-5}, url = {http://link.springer.com/chapter/10.1007/978-3-642-29746-5_16}, author = {Brunner, Clemens and Andreoni, G and Bianchi, L and Benjamin Blankertz and Breitwieser, C. and Kanoh, S. and Kothe, C. A. and Lecuyer, A. and Makeig, S and Mellinger, J. and Perego, P. and Renard, Y. and Gerwin Schalk and Susila, I.P. and Venthur, B and Mueller-Putz, G.R. and Brendan Z. Allison and Dunne, S. and Leeb, R. and Del R. Mill{\'a}n, J. and A. Nijholt} } @article {2541, title = {Review of the BCI Competition IV.}, journal = {Frontiers in Neuroprosthetics}, volume = {6}, year = {2012}, month = {07/2012}, pages = {1-31}, abstract = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.}, keywords = {BCI, brain-computer interface, competition}, doi = {10.3389/fnins.2012.00055}, url = {http://www.ncbi.nlm.nih.gov/pubmed/22811657}, author = {Tangermann, M. and Muller, K.R. and Aertsen, A. and Niels Birbaumer and Christoph Braun and Brunner, Clemens and Leeb, R. and Mehring, C. and Miller, K.J. and Mueller-Putz, G. and Nolte, G. and Pfurtscheller, G. and Preissl, H. and Gerwin Schalk and Schl{\"o}gl, A. and Vidaurre, C. and Waldert, S. and Benjamin Blankertz} } @article {2172, title = {The BCI competition III: Validating alternative approaches to actual BCI problems.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {153-9}, abstract = {

A\ brain-computer interface\ (BCI) is a system that allows its users to control external devices with\ brainactivity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user{\textquoteright}s\ brain, which produces\ brain\ activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual\ online\ use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.

}, keywords = {Algorithms, Brain, Communication Aids for Disabled, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Neuromuscular Diseases, Software Validation, Technology Assessment, Biomedical, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875642}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792282}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Krusienski, Dean J and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Pfurtscheller, Gert and Mill{\'a}n, Jos{\'e} del R and Schr{\"o}der, Michael and Niels Birbaumer} } @article {2167, title = {The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE Trans Biomed Eng}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044-51}, abstract = {Interest in developing a new method of man-to-machine communication--a brain-computer interface (BCI)--has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.}, keywords = {Adult, Algorithms, Amyotrophic Lateral Sclerosis, Artificial Intelligence, Brain, Cognition, Databases, Factual, Electroencephalography, Evoked Potentials, Humans, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, T. and Schr{\"o}der, Michael and Niels Birbaumer} } @article {3222, title = {The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.}, journal = {IEEE transactions on bio-medical engineering}, volume = {51}, year = {2004}, month = {06/2004}, pages = {1044{\textendash}1051}, abstract = {Interest in developing a new method of man-to-machine communication{\textendash}a brain-computer interface (BCI){\textendash}has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.}, keywords = {augmentative communication, BCI, beta-rhythm, brain-computer interface, EEG, ERP, imagined hand movements, lateralized readiness potential, mu-rhythm, P300, Rehabilitation, single-trial classification, slow cortical potentials}, issn = {0018-9294}, doi = {10.1109/TBME.2004.826692}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15188876}, author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, Thilo and Schr{\"o}der, Michael and Niels Birbaumer} }