G Curio

Publications of G Curio

• Towards a Direct Measure of Video Quality Perception using EEG.

  Authors: S Scholler, S Bosse, B Blankertz, G Curio, T Wiegand

  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 02/2012;

  An approach for the direct measurement of video quality change perception using electroencephalography (EEG) is presented. Subjects viewed 8s video clips while their brain activity was registeredAn approach for the direct measurement of video quality change perception using electroencephalography (EEG) is presented. Subjects viewed 8s video clips while their brain activity was registered using EEG. The video signal was either uncompressed at full length or changed from uncompressed to a lower quality level at a random time point. The distortions were introduced by a hybrid video codec. Subjects had to indicate whether or not they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the socalled P3 component) was observed at a latency of about 400-600 ms for all subjects. The voltage change positively correlated with the magnitude of the video quality change, substantiating the P3 component as a graded neural index of video quality change perception within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether or not a subject perceived a quality change. Interestingly, some video clips wherein changes were missed (i.e., not reported) by the subject were classified as quality changes, suggesting that the brain detected a change although the subject did not press a button. Concluding, abrupt changes of video quality give rise to specific components in the EEG that can be detected on a single-trial basis. Potentially, a neurotechnological approach to video assessment could lead to a more objective quantification of quality change detection, overcoming the limitations of subjective approaches (such as subjective bias and the requirement of an overt response). Furthermore, it allows for real-time applications wherein the brain response to a video clip is monitored while it is being viewed.

• Extension of non-invasive EEG into the kHz range for evoked thalamocortical activity by means of very low noise amplifiers.

  Authors: H J Scheer, T Fedele, G Curio, M Burghoff

  Physiological measurement. 11/2011; 32(12):N73-9.

  Ultrafast electroencephalographic signals, having frequencies above 500 Hz, can be observed in somatosensory evoked potential measurements. Usually, these recordings have a poor signal-to-noise ratioUltrafast electroencephalographic signals, having frequencies above 500 Hz, can be observed in somatosensory evoked potential measurements. Usually, these recordings have a poor signal-to-noise ratio (SNR) because weak signals are overlaid by intrinsic noise of much higher amplitude like that generated by biological sources and the amplifier. As an example, recordings at the scalp taken during electrical stimulation of the median nerve show a 600 Hz burst with submicro-volt amplitudes which can be extracted from noise by the use of massive averaging and digital signal processing only. We have investigated this signal by means of a very low noise amplifier made in-house (minimal voltage noise 2.7 nV Hz(-1/2), FET inputs). We examined how the SNR of the data is altered by the bandwidth and the use of amplifiers with different intrinsic amplifier noise levels of 12 and 4.8 nV Hz(-1/2), respectively. By analyzing different frequency contributions of the signal, we found an extremely weak 1 kHz component superimposed onto the well-known 600 Hz burst. Previously such high-frequency electroencephalogram responses around 1 kHz have only been observed by deep brain electrodes implanted for tremor therapy of Parkinson patients. For the non-invasive measurement of such signals, we recommend that amplifier noise should not exceed 4 nV Hz(-1/2).

• Novel applications of BCI technology: Psychophysiological optimization of working conditions in industry

  Authors: B. Venthur, B. Blankertz, M.F. Gugler, G. Curio

  Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on; 11/2010

  Brain Computer Interface (BCI) research advanced for more than forty years, providing a rich variety of sophisticated data analysis methods. Yet, most BCI studies have been restricted to theBrain Computer Interface (BCI) research advanced for more than forty years, providing a rich variety of sophisticated data analysis methods. Yet, most BCI studies have been restricted to the laboratory with controlled and undisturbed environment. BCI research was aiming at developing tools for communication and control. Recently, BCI research has broadened to explore novel applications for improved man-machine interaction. In the present study, we investigated the option to employ neurotechnology in an industrial environment for the psychophysiological optimization of working conditions in such settings. Our findings suggest that it is possible to use BCI-related analysis techniques to qualify responses of an operator by assessing the depth of cognitive processing on the basis of neuronal correlates of behaviourally relevant measures. This could lead to assistive technologies helping to avoid accidents in working environments by designing a collaborative workspace in which the environment takes into account the actual cognitive mental state of the operator.

• Using ERPs for assessing the (sub) conscious perception of noise

  Authors: A.K. Porbadnigk, J. Antons, B. Blankertz, M.S. Treder, R. Schleicher, S. Möller, G. Curio

  Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010

  In this paper, we investigate the use of event-related potentials (ERPs) as a quantitative measure for quality assessment of disturbed audio signals. For this purpose, we ran an EEG study (N=11)In this paper, we investigate the use of event-related potentials (ERPs) as a quantitative measure for quality assessment of disturbed audio signals. For this purpose, we ran an EEG study (N=11) using an oddball paradigm, during which subjects were presented with the phoneme /a/, superimposed with varying degrees of signal-correlated noise. Based on this data set, we address the question to which degree the degradation of the auditory stimuli is reflected on a neural level, even if the disturbance is below the threshold of conscious perception. For those stimuli that are consciously recognized as being disturbed, we suggest the use of the amplitude and latency of the P300 component for assessing the level of disturbance. For disturbed stimuli for which the noise is not perceived consciously, we show for two subjects that a classifier based on shrinkage LDA can be applied successfully to single out stimuli, for which the noise was presumably processed subconsciously.

• Predicting BCI performance to study BCI illiteracy

  Authors: T. Dickhaus, C. Sannelli, K.R. M\"uller, G. Curio, B. Blankertz

  BMC Neuroscience. 01/2009; 10:P84.

• The Berlin Brain--Computer Interface: Accurate Performance From First-Session in BCI-NaÏve Subjects

  Authors: B. Blankertz, F. Losch, M. Krauledat, G. Dornhege, G. Curio, K.-R. Muller

  Biomedical Engineering, IEEE Transactions on. 11/2008;

  The Berlin brain-computer interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensionalThe Berlin brain-computer interface (BBCI) project develops a noninvasive BCI system whose key features are: 1) the use of well-established motor competences as control paradigms; 2) high-dimensional features from multichannel EEG; and 3) advanced machine-learning techniques. Spatio-spectral changes of sensorimotor rhythms are used to discriminate imagined movements (left hand, right hand, and foot). A previous feedback study [M. Krauledat, K.-R. Muller, and G. Curio. (2007) The non-invasive Berlin brain-computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage . [Online]. 37(2) , pp. 539--550. Available: http://dx.doi.org/10.1016/j.neuroimage.2007.01.051] with ten subjects provided preliminary evidence that the BBCI system can be operated at high accuracy for subjects with less than five prior BCI exposures. Here, we demonstrate in a group of 14 fully BCI-naive subjects that 8 out of 14 BCI novices can perform at >84% accuracy in their very first BCI session, and a further four subjects at >70%. Thus, 12 out of 14 BCI-novices had significant above-chance level performances without any subject training even in the first session, as based on an optimized EEG analysis by advanced machine-learning algorithms.

• Anticipatory activity in the human thalamus is predictive of reaction times.

  Authors: V.V. Nikulin, F Marzinzik, M Wahl, G H Schneider, A Kupsch, G Curio, F Klostermann

  Neuroscience. 10/2008; 155(4):1275-83.

  Responding to environmental stimuli in a fast manner is a fundamental behavioral capacity. The pace at which one responds is known to be predetermined by cortical areas, but it remains to be shown ifResponding to environmental stimuli in a fast manner is a fundamental behavioral capacity. The pace at which one responds is known to be predetermined by cortical areas, but it remains to be shown if subcortical structures also take part in defining motor swiftness. As the thalamus has previously been implicated in behavioral control, we tested if neuronal activity at this level could also predict the reaction time of upcoming movements. To this end we simultaneously recorded electrical brain activity from the scalp and the ventral intermediate nucleus (VIM) of the thalamus in patients undergoing thalamic deep brain stimulation. Based on trial-to-trial analysis of a Go/NoGo task, we demonstrate that both cortical and thalamic neuronal activity prior to the delivery of upcoming Go stimulus correlates with the reaction time. This result goes beyond the demonstration of thalamic activity being associated with but potentially staying invariant to motor performance. In contrast, it indicates that the latencies at which we respond to environmental stimuli are not exclusively related to cortical pre-movement states but are also correlated with anticipatory thalamic activity.

• Effects of subthalamic deep brain stimulation on dysarthrophonia in Parkinson's disease.

  Authors: F Klostermann, F Ehlen, J Vesper, K Nubel, M Gross, F Marzinzik, G Curio, T Sappok

  Journal of neurology, neurosurgery, and psychiatry. 06/2008; 79(5):522-9.

  BACKGROUND: Motor deficits in Parkinson's disease (PD) are reduced by deep brain stimulation (DBS) of the subthalamic nucleus (STN), but the impact of this therapy on dysarthrophonic problems in PDBACKGROUND: Motor deficits in Parkinson's disease (PD) are reduced by deep brain stimulation (DBS) of the subthalamic nucleus (STN), but the impact of this therapy on dysarthrophonic problems in PD remains controversial. We therefore aimed to disentangle the effects of STN DBS on the speech skills of long-term treated patients. METHODS: Under continued medication, speech and motor functions of 19 patients with PD with bilateral STN DBS were studied when their therapeutic stimulation was active (STIM-ON) versus switched off (STIM-OFF). Per condition, perceptual speech ratings were given by: (i) the patients themselves, (ii) the treating physician, and (iii) professional speech therapists. Furthermore, single speech parameters were measured with a battery of technical exams in both STIM-ON and STIM-OFF. RESULTS: STN DBS significantly worsened speech performance according to all perceptual rating methods applied. In contrast, technical measures showed DBS-induced improvements of single speech dimensions affected by the PD-specific motor disorder. These changes occurred independently of the reduction of motor impairment, which was consistently effectuated by STN DBS. CONCLUSION: In parallel to the beneficial effects on the motor symptoms of PD, STN DBS reduces designated disease-inherent dysarthrophonic symptoms, such as glottic tremor. However, these actions on speech are predominantly outweighed by the general dysarthrogenic effects of STN DBS, probably based on a decline of complex (eg, prosodic) functions. Thus, stimulation-induced speech impairment should be considered a likely problem in the course of this treatment.

• Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring

  Authors: K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, B. Blankertz

  J. Neurosci. Methods. 01/2008; 167:82--90.

• The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects

  Authors: B. Blankertz, F. Losch, M. Krauledat, G. Dornhege, G. Curio, K. R. Müller

  IEEE Trans. Biomed. Eng. 01/2008;

• DC-magnetoencephalography and time-resolved near-infrared spectroscopy combined to study neuronal and vascular brain responses.

  Authors: T H Sander, A Liebert, B M Mackert, H Wabnitz, S Leistner, G Curio, M Burghoff, R Macdonald, L Trahms

  Physiological measurement. 07/2007; 28(6):651-64.

  The temporal relation between vascular and neuronal responses of the brain to external stimuli is not precisely known. For a better understanding of the neuro-vascular coupling changes in cerebralThe temporal relation between vascular and neuronal responses of the brain to external stimuli is not precisely known. For a better understanding of the neuro-vascular coupling changes in cerebral blood volume and oxygenation have to be measured simultaneously with neuronal currents. With this motivation modulation dc-magnetoencephalography was combined with multi-channel time-resolved near-infrared spectroscopy to simultaneously monitor neuronal and vascular parameters on a scale of seconds. Here, the technique is described, how magnetic and optical signals can be measured simultaneously. In a simple motor activation paradigm (alternating 30 s of finger movement with 30 s of rest for 40 min) both signals were recorded non-invasively over the motor cortex of eight subjects. The off-line averaged signals from both modalities showed distinct stimulation related changes. By plotting changes in oxy- or deoxyhaemoglobin as a function of magnetic field a characteristic trajectory was created, which was similar to a hysteresis loop. A parametric analysis allowed quantitative results regarding the timing of coupling: the vascular signal increased significantly slower than the neuronal signal.

• The non-invasive Berlin Brain--Computer Interface: Fast acquisition of effective performance in untrained subjects

  Authors: B. Blankertz, G. Dornhege, M. Krauledat, K.R. Müller, G. Curio

  Neuroimage. 01/2007; 37:539--550.

• Improving human performance in a real operating environment through real-time mental workload detection

  Authors: J. Kohlmorgen, G. Dornhege, M. Braun, B. Blankertz, K. R. Müller, G. Curio, K. Hagemann, A. Bruns, M. Schrauf, W. Kincses

  01/2007: pages 409–422;

• The Berlin Brain-Computer Interface (BBCI)--towards a new communication channel for online control in gaming applications

  Authors: R. Krepki, B. Blankertz, G. Curio, K. R. Müller

  Multimedia Tools and Applications. 01/2007; 33:73--90.

• Berlin Brain--Computer Interface—The HCI communication channel for discovery

  Authors: R. Krepki, G. Curio, B. Blankertz, K.R. Müller

  International Journal of Human-Computer Studies. 01/2007; 65:460--477.

• Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing

  Authors: G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, K.-R. Muller

  Biomedical Engineering, IEEE Transactions on. 12/2006;

  Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore theyBrain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms

• The Berlin brain-computer interface: EEG-based communication without subject training

  Authors: B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Muller, V. Kunzmann, F. Losch, G. Curio

  Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 07/2006;

  The Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensionalThe Berlin Brain-Computer Interface (BBCI) project develops a noninvasive BCI system whose key features are 1) the use of well-established motor competences as control paradigms, 2) high-dimensional features from 128-channel electroencephalogram (EEG), and 3) advanced machine learning techniques. As reported earlier, our experiments demonstrate that very high information transfer rates can be achieved using the readiness potential (RP) when predicting the laterality of upcoming left- versus right-hand movements in healthy subjects. A more recent study showed that the RP similarly accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb. In a complementary approach, oscillatory features are used to discriminate imagined movements (left hand versus right hand versus foot). In a recent feedback study with six healthy subjects with no or very little experience with BCI control, three subjects achieved an information transfer rate above 35 bits per minute (bpm), and further two subjects above 24 and 15 bpm, while one subject could not achieve any BCI control. These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results with very well-trained subjects operating other BCI systems.

• On-line differentiation of neuroelectric activities: algorithms and applications.

  Authors: M. Krauledat, B. Blankertz, G. Dornhege, M Schröder, G Curio, K.-R. Müller

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 02/2006; Suppl:6715-9.

  This paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCIThis paper discusses machine learning methods and their application to Brain-Computer Interfacing. A particular focus is placed on linear classification methods which can be applied in the BCI context. Finally, we provide an overview of the Berlin-Brain Computer Interface (BBCI).

• Optimizing spatio-temporal filters for improving brain-computer interfacing

  Authors: G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, K. R. Müller

  01/2006

• Combined optimization of spatial and temporal filters for improving brain-computer interfacing

  Authors: G. Dornhege, B. Blankertz, M. Krauledat, F. Losch, G. Curio, K.R. Müller

  IEEE Trans. Biomed. Eng. 01/2006; 53:2274--2281.