%0 Journal Article %J Front Hum Neurosci %D 2014 %T ECoG high gamma activity reveals distinct cortical representations of lyrics passages, harmonic and timbre-related changes in a rock song. %A Sturm, Irene %A Benjamin Blankertz %A Potes, Cristhian %A Gerwin Schalk %A Curio, Gabriel %K acoustic features %K electrocorticography (ECoG) %K high gamma %K music processing %K natural music %X

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's opus is not manipulated for experimental purposes, engages a listener'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.

%B Front Hum Neurosci %V 8 %P 798 %8 10/2014 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/25352799 %R 10.3389/fnhum.2014.00798 %0 Journal Article %J IEEE Trans Biomed Eng %D 2004 %T The BCI Competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. %A Benjamin Blankertz %A Müller, Klaus-Robert %A Curio, Gabriel %A Theresa M Vaughan %A Gerwin Schalk %A Jonathan Wolpaw %A Schlögl, Alois %A Neuper, Christa %A Pfurtscheller, Gert %A Hinterberger, T. %A Schröder, Michael %A Niels Birbaumer %K Adult %K Algorithms %K Amyotrophic Lateral Sclerosis %K Artificial Intelligence %K Brain %K Cognition %K Databases, Factual %K Electroencephalography %K Evoked Potentials %K Humans %K Reproducibility of Results %K Sensitivity and Specificity %K User-Computer Interface %X 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. %B IEEE Trans Biomed Eng %V 51 %P 1044-51 %8 06/2004 %G eng %N 6 %R 10.1109/TBME.2004.826692 %0 Journal Article %J IEEE transactions on bio-medical engineering %D 2004 %T The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials. %A Benjamin Blankertz %A Müller, Klaus-Robert %A Curio, Gabriel %A Theresa M Vaughan %A Gerwin Schalk %A Jonathan Wolpaw %A Schlögl, Alois %A Neuper, Christa %A Pfurtscheller, Gert %A Hinterberger, Thilo %A Schröder, Michael %A Niels Birbaumer %K augmentative communication %K BCI %K beta-rhythm %K brain-computer interface %K EEG %K ERP %K imagined hand movements %K lateralized readiness potential %K mu-rhythm %K P300 %K Rehabilitation %K single-trial classification %K slow cortical potentials %X 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. %B IEEE transactions on bio-medical engineering %V 51 %P 1044–1051 %8 06/2004 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15188876 %R 10.1109/TBME.2004.826692