Correlation transmission of spiking neurons is boosted by synchronous input : From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011

Published by BioMed Central Schultze-Kraft, Matthias ; Diesmann, Markus ; Grün, Sonja ; Helias, Moritz : Correlation transmission of spiking neurons is boosted by synchronous input : From Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. - In: BMC Neuroscience. - ISSN 1471-2202 (online). - 12 (2011), suppl. 1, P144. - doi:10.1186/1471-2202-12-S1-P144

Prediction of difficulty levels in video games from ongoing EEG

Real-time assessment of mental workload from EEG plays an important role in enhancing symbiotic interaction of human operators in immersive environments. In this study we thus aimed at predicting the difficulty level of a video game a person is playing at a particular moment from the ongoing EEG activity. Therefore, we made use of power modulations in the theta (4–7 Hz) and alpha (8–13 Hz) frequency bands of the EEG which are known to reflect cognitive workload. Since the goal was to predict from multiple difficulty levels, established binary classification approaches are futile. Here, we employ a novel spatial filtering method (SPoC) that finds spatial filters such that their corresponding bandpower dynamics maximally covary with a given target variable, in this case the difficulty level. EEG was recorded from 6 participants playing a modified Tetris game at 10 different difficulty levels. We found that our approach predicted the levels with high accuracy, yielding a mean prediction error of less than one level.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

Unsupervised classification of operator workload from brain signals

Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects' error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeEC/FP7/625991/EU/Hyperscanning 2.0 Analyses of Multimodal Neuroimaging Data: Concept, Methods and Applications/HYPERSCANNING 2.0DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systeme

Noise Suppression and Surplus Synchrony by Coincidence Detection

The functional significance of correlations between action potentials of neurons is still a matter of vivid debates. In particular it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to closely time-locked spiking activity of pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high afferent correlation, in the presence of synchrony a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks

Hirn-Computer-Schnittstellen für die kognitiven Neurowissenschaften und für fortgeschrittene Detektion mentaler Zustände

Advancements in machine learning in combination with fundamental research in cognitive neuroscience have put forth application areas for brain-computer interfaces (BCIs) that go beyond communication and control. The ability to decode covert mental states and intentions from the electroencephalogram (EEG) in real-time – hence, to study the "brain at work" – establishes the basis for multifaceted applications of non-control BCIs. In this thesis, the use of such BCIs is demonstrated with two independent studies which both have different research directions and serve different purposes. While the first study follows what has been the traditional path of BCI research, namely the development of an application for people, the second study strikes a new path by engaging in the hitherto unsought approach to use a closed-loop BCI as a research tool for cognitive neuroscience. The first study aims for the classification of operator workload as it is expected in many real-life workplace environments. Brain-signal based workload predictors, based on modulations of the power of theta and alpha oscillations in the EEG associated with workload changes, were explored. The predictors differed with respect to the level of label information required for training, including an entirely unsupervised approach. This was made possible by employing stateof- the-art EEG spatial filtering methods from machine learning. Mean classification accuracies above 90% were achieved with the supervised predictors and 82% with the unsupervised approach. The findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable. The second study investigates the role of the readiness potential (RP), a slow cortical potential that starts more than 1 second before spontaneous, voluntary movements. Despite decades-long research in cognitive neuroscience, it has yet remained unclear whether the onset of the RP triggers a chain of events that unfolds in time and cannot be cancelled or whether people can cancel movements after onset of the RP. In this study, this question was addressed in a realtime experiment in which subjects were required to terminate their decision to move upon seeing a stop signal. This signal was elicited by a BCI that had been trained to detect RPs in the ongoing EEG. It was found that subjects could indeed cancel intended movements after the onset of the RP, however only up to a point of no return at approximately 200 ms before movement onset. The finding that the onset of the RP does not trigger a ballistic process that cannot be stopped throws some light on the controversial debate regarding the role of the RP in movement preparation.Fortschritte im Maschinellen Lernen und Erkenntnisse in den Kognitiven Neurowissenschaften haben neue Anwendungsmöglichkeiten für Hirn-Computer-Schnittstellen (HCS) hervorgebracht, die über die gängigen Kommunikationsanwendungen hinaus gehen und auf der Echtzeit-Erkennung verdeckter mentaler Zustände und Absichten im Elektroenzephalogramm (EEG) basieren. Diese Dissertation demonstriert dies anhand von zwei unabhängigen Studien, die jeweils unterschiedliche Forschungsziele haben. Während sich die erste Studie mit der traditionellen Entwicklung einer personenbezogenen Anwendung beschäftigt, schlägt die zweite Studie einen neuen Pfad ein und verfolgt das Ziel, HCS direkt als Werkzeug für Forschung in den Kognitiven Neurowissenschaften einsetzen zu können. Die erste Studie strebt die Klassifizierung von Arbeitslast an, wie sie in vielen Arbeitsplatzumgebungen zu erwarten ist. Dazu wurden verschiedene Arbeitslast-Prädiktoren untersucht, die auf Energiemodulationen von theta- und alpha-Oszillationen im EEG beruhen, welche mit Änderungen von Arbeitslast einhergehen, einschliesslich eines komplett nicht-überwachten Prädiktors. Um dies zu ermöglichen, wurden allerneueste Methodenentwicklungen aus dem Maschinellen Lernen benutzt. Mit den überwachten Methoden wurden durchschnittliche Klassifizierungsgenauigkeiten von über 90% erreicht, mit dem nicht-überwachten Ansatz 82%. Diese Ergebnisse zeigen, dass Arbeitslast-Zustände anhand von Hirnsignalen erfolgreich differenziert werden können, selbst wenn zunehmend weniger Information über das experimentelle Paradigma benutzt wird. Damit ist der Weg geebnet für Praxisanwendungen, wo Kennsatz-Information oft verrauscht oder erst gar nicht vorhanden ist. Die zweite Studie untersucht die Funktion des Bereitschaftspotentials (BP), ein EEG-Signal, das mehr als 1 Sekunde vor spontanen, absichtlichen Bewegungen beginnt. Trotz jahrzentelanger Forschung herrscht noch Unklarheit darüber, ob das Einsetzen des BP eine Ereignisskette in Gang setzt, die sich nicht mehr aufhalten lässt oder ob Menschen eine Bewegung selbst nach Einsetzen des BP stoppen können. Diese Frage wurde in einem Echtzeit-Experiment untersucht, in dem Versuchsteilnehmer aufgefordert wurden, eine Entscheidung für eine Bewegung zurück zu ziehen, sobald ein Stoppsignal erschien. Dieses Signal wurde von einer HCS gesteuert, die zuvor darauf trainiert worden war, das Einsetzen von BPs im EEG zu erkennen. Das Experiment ergab, dass Versuchsteilnehmer Bewegungen selbst nach Einsetzen des BP stoppen konnten, jedoch nur bis zu einem Umkehrgrenzpunkt, der bei ungefähr 200 ms vor Einsetzen der Bewegung lag. Die Erkenntniss, dass das Einsetzen des BP nicht einen ballistischen, d.h. unaufhaltbaren, Prozess in Gang setzt, leistet einen Beitrag zur Aufklärung der kontroversen Debatte bezüglich der Rolle des BP

Mechanistic model of enhanced correlation transmission by synchronous input events.

<p><b>A</b> The detailed model discussed in the results section is simplified two-fold. 1) We consider binary neurons with a static non-linearity . 2) We distinguish two representative scenarios with different models for the common input: : Gaussian white noise with variance , representing the case without synchrony, or : a binary stochastic process with constant amplitude , mimicking the synchronous arrival of synaptic events. In both scenarios in addition each neuron receives independent Gaussian input. <b>B</b> Marginal distribution of the total input to a single neuron for input (gray) and (black) and for . In input the binary process alternates between (with probability ) and (with probability ), resulting in a bimodal marginal distribution. The mean activity of one single neuron is given by the probability mass above threshold . We choose the variances and of the disjoint Gaussian fluctuating input such that the mean activity is the same in both scenarios. <b>C</b> Output correlation as a function of the input correlation (see A) between the total inputs and . Probability is chosen such that inputs and result in the same input correlation . The four points marked by circles correspond to the panels D–G. <b>D</b>–<b>G</b> Joint probability density of the inputs , to both neurons. For two different values of the lower row (E,G) shows the scenario , the upper row (D,F) the scenario . Note that panel B is the projection of the joint densities in F and G to one axis. Brighter gray levels indicate higher probability density; same gray scale for all four panels.</p

A pair of integrate-and-fire model neurons driven by partially shared and correlated presynaptic events.

<p><b>A</b> Each of the neurons and receives input from sources, of which are excitatory and are inhibitory. Both neurons share a fraction of their excitatory and inhibitory sources, whereas the fraction is independent for each neuron. Schematically represented spike trains on the left of the diagram show the excitatory part of the input; the inhibitory input is only indicated. A single source emits spike events with a firing rate , with marginal Poisson statistics. Correlated spiking is introduced in the common excitatory sources to both neurons. This pairwise correlation is realized by means of a multiple interaction process (MIP) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002904#pcbi.1002904-Kuhn1" target="_blank">[39]</a> that yields a correlation coefficient of between any pairs of sources. In absence of a threshold, the summed input drives the membrane potential to a particular working point described by its mean and standard deviation and the correlation coefficient between the free membrane potentials , of both neurons. In presence of a threshold mean and variance of the membrane potential determine the output firing rate and their correlation in addition determines the output correlation , calculated by (2). <b>B</b>–<b>E</b> Direct simulation was performed using different values of common input fraction and four fixed values of input spike synchrony (as denoted in E). Each combination of and was simulated for seconds; gray coded data points show the average over independent realizations. Remaining parameters are given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002904#pcbi-1002904-t001" target="_blank">Table 1</a>. Solid lines in B and C are calculated as (5) and (6), respectively. In C, for convenience, is normalized by the common input fraction , so that in absence of synchrony (). E shows the output spike synchrony .</p