True zero-training brain-computer interfacing: an online study

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model

The brain as a generative model: information-theoretic surprise in learning and action

Our environment is rich with statistical regularities, such as a sudden cold gust of wind indicating a potential change in weather. A combination of theoretical work and empirical evidence suggests that humans embed this information in an internal representation of the world. This generative model is used to perform probabilistic inference, which may be approximated through surprise minimization. This process rests on current beliefs enabling predictions, with expectation violation amounting to surprise. Through repeated interaction with the world, beliefs become more accurate and grow more certain over time. Perception and learning may be accounted for by minimizing surprise of current observations, while action is proposed to minimize expected surprise of future events. This framework thus shows promise as a common formulation for different brain functions. The work presented here adopts information-theoretic quantities of surprise to investigate both perceptual learning and action. We recorded electroencephalography (EEG) of participants in a somatosensory roving-stimulus paradigm and performed trial-by-trial modeling of cortical dynamics. Bayesian model selection suggests early processing in somatosensory cortices to encode confidence-corrected surprise and subsequently Bayesian surprise. This suggests the somatosensory system to signal surprise of observations and update a probabilistic model learning transition probabilities. We also extended this framework to include audition and vision in a multi-modal roving-stimulus study. Next, we studied action by investigating a sensitivity to expected Bayesian surprise. Interestingly, this quantity is also known as information gain and arises as an incentive to reduce uncertainty in the active inference framework, which can correspond to surprise minimization. In comparing active inference to a classical reinforcement learning model on the two-step decision-making task, we provided initial evidence for active inference to better account for human model-based behaviour. This appeared to relate to participants’ sensitivity to expected Bayesian surprise and contributed to explaining exploration behaviour not accounted for by the reinforcement learning model. Overall, our findings provide evidence for information-theoretic surprise as a model for perceptual learning signals while also guiding human action.Unsere Umwelt ist reich an statistischen Regelmäßigkeiten, wie z. B. ein plötzlicher kalter Windstoß, der einen möglichen Wetterumschwung ankündigt. Eine Kombination aus theoretischen Arbeiten und empirischen Erkenntnissen legt nahe, dass der Mensch diese Informationen in eine interne Darstellung der Welt einbettet. Dieses generative Modell wird verwendet, um probabilistische Inferenz durchzuführen, die durch Minimierung von Überraschungen angenähert werden kann. Der Prozess beruht auf aktuellen Annahmen, die Vorhersagen ermöglichen, wobei eine Verletzung der Erwartungen einer Überraschung gleichkommt. Durch wiederholte Interaktion mit der Welt nehmen die Annahmen mit der Zeit an Genauigkeit und Gewissheit zu. Es wird angenommen, dass Wahrnehmung und Lernen durch die Minimierung von Überraschungen bei aktuellen Beobachtungen erklärt werden können, während Handlung erwartete Überraschungen für zukünftige Beobachtungen minimiert. Dieser Rahmen ist daher als gemeinsame Bezeichnung für verschiedene Gehirnfunktionen vielversprechend. In der hier vorgestellten Arbeit werden informationstheoretische Größen der Überraschung verwendet, um sowohl Wahrnehmungslernen als auch Handeln zu untersuchen. Wir haben die Elektroenzephalographie (EEG) von Teilnehmern in einem somatosensorischen Paradigma aufgezeichnet und eine trial-by-trial Modellierung der kortikalen Dynamik durchgeführt. Die Bayes'sche Modellauswahl deutet darauf hin, dass frühe Verarbeitung in den somatosensorischen Kortizes confidence corrected surprise und Bayesian surprise kodiert. Dies legt nahe, dass das somatosensorische System die Überraschung über Beobachtungen signalisiert und ein probabilistisches Modell aktualisiert, welches wiederum Wahrscheinlichkeiten in Bezug auf Übergänge zwischen Reizen lernt. In einer weiteren multimodalen Roving-Stimulus-Studie haben wir diesen Rahmen auch auf die auditorische und visuelle Modalität ausgeweitet. Als Nächstes untersuchten wir Handlungen, indem wir die Empfindlichkeit gegenüber der erwarteten Bayesian surprise betrachteten. Interessanterweise ist diese informationstheoretische Größe auch als Informationsgewinn bekannt und stellt, im Rahmen von active inference, einen Anreiz dar, Unsicherheit zu reduzieren. Dies wiederum kann einer Minimierung der Überraschung entsprechen. Durch den Vergleich von active inference mit einem klassischen Modell des Verstärkungslernens (reinforcement learning) bei der zweistufigen Entscheidungsaufgabe konnten wir erste Belege dafür liefern, dass active inference menschliches modellbasiertes Verhalten besser abbildet. Dies scheint mit der Sensibilität der Teilnehmer gegenüber der erwarteten Bayesian surprise zusammenzuhängen und trägt zur Erklärung des Explorationsverhaltens bei, das jedoch nicht vom reinforcement learning-Modell erklärt werden kann. Insgesamt liefern unsere Ergebnisse Hinweise für Formulierungen der informationstheoretischen Überraschung als Modell für Signale wahrnehmungsbasierten Lernens, die auch menschliches Handeln steuern

Mismatch responses: Probing probabilistic inference in the brain

Sensory signals are governed by statistical regularities and carry valuable information about the unfolding of environmental events. The brain is thought to capitalize on the probabilistic nature of sequential inputs to infer on the underlying (hidden) dynamics driving sensory stimulation. Mis-match responses (MMRs) such as the mismatch negativity (MMN) and the P3 constitute prominent neuronal signatures which are increasingly interpreted as reflecting a mismatch between the current sensory input and the brain’s generative model of incoming stimuli. As such, MMRs might be viewed as signatures of probabilistic inference in the brain and their response dynamics can provide insights into the underlying computational principles. However, given the dominance of the auditory modality in MMR research, the specifics of brain responses to probabilistic sequences across sensory modalities and especially in the somatosensory domain are not well characterized. The work presented here investigates MMRs across the auditory, visual and somatosensory modality by means of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). We designed probabilistic stimulus sequences to elicit and characterize MMRs and employed computational modeling of response dynamics to inspect different aspects of the brain’s generative model of the sensory environment. In the first study, we used a volatile roving stimulus paradigm to elicit somatosensory MMRs and performed single-trial modeling of EEG signals in sensor and source space. Model comparison suggested that responses reflect Bayesian inference based on the estimation of transition probability and limited information integration of the recent past in order to adapt to a changing environment. The results indicated that somatosensory MMRs reflect an initial mismatch between sensory input and model beliefs represented by confidence-corrected surprise (CS) followed by model adjustment dynamics represented by Bayesian surprise (BS). For the second and third study we designed a tri-modal roving stimulus paradigm to delineate modality specific and modality general features of mismatch processing. Computational modeling of EEG signals in study 2 suggested that single-trial dynamics reflect Bayesian inference based on estimation of uni-modal transition probabilities as well as cross-modal conditional dependencies. While early mismatch processing around the MMN tended to reflect CS, later MMRs around the P3 rather reflect BS, in correspondence to the somatosensory study. Finally, the fMRI results of study 3 showed that MMRs are generated by an interaction of modality specific regions in higher order sensory cortices and a modality general fronto-parietal network. Inferior parietal regions in particular were sensitive to expectation violations with respect to the cross-modal contingencies in the stimulus sequences. Overall, our results indicate that MMRs across the senses reflect processes of probabilistic inference in a complex and inherently multi-modal environment.Sensorische Signale sind durch statistische Regularitäten bestimmt und beinhalten wertvolle Informationen über die Entwicklung von Umweltereignissen. Es wird angenommen, dass das Gehirn die Wahrscheinlichkeitseigenschaften sequenzieller Reize nutzt um auf die zugrundeliegenden (verborgenen) Dynamiken zu schließen, welche sensorische Stimulation verursachen. Diskrepanz-Reaktionen ("Mismatch responses"; MMRs) wie die "mismatch negativity" (MMN) und die P3 sind bekannte neuronale Signaturen die vermehrt als Signale einer Diskrepanz zwischen der momentanen sensorischen Einspeisung und dem generativen Modell, welches das Gehirn von den eingehenden Reizen erstellt angesehen werden. Als solche können MMRs als Signaturen von wahrscheinlichkeitsbasierter Inferenz im Gehirn betrachtet werden und ihre Reaktionsdynamiken können Einblicke in die zugrundeliegenden komputationalen Prinzipien geben. Angesichts der Dominanz der auditorischen Modalität in der MMR-Forschung, sind allerdings die spezifischen Eigenschaften von Hirn-Reaktionen auf Wahrscheinlichkeitssequenzen über sensorische Modalitäten hinweg und vor allem in der somatosensorischen Modalität nicht gut charakterisiert. Die hier vorgestellte Arbeit untersucht MMRs über die auditorische, visuelle und somatosensorische Modalität hinweg anhand von Elektroenzephalographie (EEG) und funktioneller Magnetresonanztomographie (fMRT). Wir gestalteten wahrscheinlichkeitsbasierte Reizsequenzen, um MMRs auszulösen und zu charakterisieren und verwendeten komputationale Modellierung der Reaktionsdynamiken, um verschiedene Aspekte des generativen Modells des Gehirns von der sensorischen Umwelt zu untersuchen. In der ersten Studie verwendeten wir ein volatiles "Roving-Stimulus"-Paradigma, um somatosensorische MMRs auszulösen und modellierten die Einzel-Proben der EEG-Signale im sensorischen und Quell-Raum. Modellvergleiche legten nahe, dass die Reaktionen Bayes’sche Inferenz abbilden, basierend auf der Schätzung von Transitionswahrscheinlichkeiten und limitierter Integration von Information der jüngsten Vergangenheit, welche eine Anpassung an Umweltänderungen ermöglicht. Die Ergebnisse legen nahe, dass somatosen-sorische MMRs eine initiale Diskrepanz zwischen sensorischer Einspeisung und Modellüberzeugung reflektieren welche durch "confidence-corrected surprise" (CS) repräsentiert ist, gefolgt von Modelanpassungsdynamiken repräsentiert von "Bayesian surprise" (BS). Für die zweite und dritte Studie haben wir ein Tri-Modales "Roving-Stimulus"-Paradigma gestaltet, um modalitätsspezifische und modalitätsübergreifende Eigenschaften von Diskrepanzprozessierung zu umreißen. Komputationale Modellierung von EEG-Signalen in Studie 2 legte nahe, dass Einzel-Proben Dynamiken Bayes’sche Inferenz abbilden, basierend auf der Schätzung von unimodalen Transitionswahrscheinlichkeiten sowie modalitätsübergreifenden bedingten Abhängigkeiten. Während frühe Diskrepanzprozessierung um die MMN dazu tendierten CS zu reflektieren, so reflektierten spätere MMRs um die P3 eher BS, in Übereinstimmung mit der somatosensorischen Studie. Abschließend zeigten die fMRT-Ergebnisse der Studie 3 dass MMRs durch eine Interaktion von modalitätsspezifischen Regionen in sensorischen Kortizes höherer Ordnung mit einem modalitätsübergreifenden fronto-parietalen Netzwerk generiert werden. Inferior parietale Regionen im Speziellen waren sensitiv gegenüber Erwartungsverstoß in Bezug auf die modalitätsübergreifenden Wahrscheinlichkeiten in den Reizsequenzen. Insgesamt weisen unsere Ergebnisse darauf hin, dass MMRs über die Sinne hinweg Prozesse von wahrscheinlichkeitsbasierter Inferenz in einer komplexen und inhärent multi-modalen Umwelt darstellen

Distilling the neural correlates of conscious somatosensory perception

The ability to consciously perceive the world profoundly defines our lives as human beings. Somehow, our brains process information in a way that allows us to become aware of the images, sounds, touches, smells, and tastes surrounding us. Yet our understanding of the neurobiological processes that generate perceptual awareness is very limited. One of the most contested questions in the neuroscientific study of conscious perception is whether awareness arises from the activity of early sensory brain regions, or instead requires later processing in widespread supramodal networks. It has been suggested that the conflicting evidence supporting these two perspectives may be the result of methodological confounds in classical experimental tasks. In order to infer participants’ perceptual awareness in these tasks, they need to report the contents of their perception. This means that the neural signals underlying the emergence of perceptual awareness often cannot be dissociated from pre- and postperceptual processes. Consequently, some of the previously observed effects may not be correlates of awareness after all but instead may have resulted from task requirements. In this thesis, I investigate this possibility in the somatosensory modality. To scrutinise the task dependence of the neural correlates of somatosensory awareness, I developed an experimental paradigm that controls for the most common experimental confounds. In a somatosensory-visual matching task, participants were required to detect electrical target stimuli at ten different intensity levels. Instead of reporting their perception directly, they compared their somatosensory percepts to simultaneously presented visual cues that signalled stimulus presence or absence and then reported a match or mismatch accordingly. As a result, target detection was decorrelated from working memory and reports, the behavioural relevance of detected and undetected stimuli was equated, the influence of attentional processes was mitigated, and perceptual uncertainty was varied in a controlled manner. Results from a functional magnetic resonance imaging (fMRI) study and an electroencephalography (EEG) study showed that, when controlled for task demands, the neural correlates of somatosensory awareness were restricted to relatively early activity (~150 ms) in secondary somatosensory regions. In contrast, late activity (>300 ms) indicative of processing in frontoparietal networks occurred irrespective of stimulus awareness, and activity in anterior insular, anterior cingulate, and supplementary motor cortex was associated with processing perceptual uncertainty and reports. These results add novel evidence to the early-local vs. late-global debate and favour the view that perceptual awareness emerges at the level of modality-specific sensory cortices.Die Fähigkeit zur bewussten Wahrnehmung bestimmt maßgeblich unser Selbstbild als Menschen. Unser Gehirn verarbeitet Informationen auf eine Weise, die es uns ermöglicht, uns der Bilder, Töne, Berührungen, Gerüche und Geschmäcker, die uns umgeben, bewusst zu werden. Unser Verständnis davon, wie neurobiologische Prozesse diese bewusste Wahrnehmung erzeugen, ist jedoch noch sehr begrenzt. Eine der umstrittensten Fragen in der neurowissenschaftlichen Erforschung des perzeptuellen Bewusstseins besteht darin, ob die bewusste Wahrnehmung aus der Aktivität früher sensorischer Hirnregionen entsteht, oder aber die spätere Prozessierung in ausgedehnten supramodalen Netzwerken erfordert. Eine mögliche Erklärung für die widersprüchlichen Ergebnisse, die diesen beiden Perspektiven zugrunde liegen, wird in methodologischen Störfaktoren vermutet, die in klassischen experimentellen Paradigmen auftreten können. Um auf die Wahrnehmung der Versuchspersonen schließen zu können, müssen diese den Inhalt ihrer Wahrnehmung berichten. Das führt dazu, dass neuronale Korrelate bewusster Wahrnehmung häufig nicht sauber von prä- und postperzeptuellen Prozessen getrennt werden können. Folglich könnten einige der zuvor beobachteten Effekte, anstatt tatsächlich bewusste Wahrnehmung widerzuspiegeln, aus den Anforderungen experimenteller Paradigmen entstanden sein. In dieser Arbeit untersuche ich diese Möglichkeit in der somatosensorischen Modalität. Um zu überprüfen, inwiefern neuronale Korrelate bewusster somatosensorischer Wahrnehmung von den Anforderungen experimenteller Aufgaben abhängen, habe ich ein Paradigma entwickelt, dass die häufigsten experimentellen Störfaktoren kontrolliert. In einer somatosensorisch-visuellen Vergleichsaufgabe mussten die Versuchspersonen elektrische Zielreize in zehn verschiedenen Intensitätsstufen detektieren. Anstatt diese jedoch direkt zu berichten, sollten sie ihre somatosensorischen Perzepte mit gleichzeitig präsentierten visuellen Symbolen vergleichen, die entweder Reizanwesenheit oder -abwesenheit signalisierten. Entsprechend wurde dann eine Übereinstimmung oder Nichtübereinstimmung berichtet. Dadurch wurde die Reizwahrnehmung von Arbeitsgedächtnis und Berichterstattung dekorreliert, die Verhaltensrelevanz detektierter und nicht detektierter Reize gleichgesetzt, der Einfluss von Aufmerksamkeitsprozessen reduziert und die mit der Detektion verbundene Unsicherheit auf kontrollierte Weise variiert. Die Ergebnisse aus einer funktionellen Magnetresonanztomographie (fMRT)-Studie und einer Elektroenzephalographie (EEG)-Studie zeigen, dass die neuronalen Korrelate bewusster somatosensorischer Wahrnehmung auf relativ frühe Aktivität (~150 ms) in sekundären somatosensorischen Regionen beschränkt sind, wenn experimentelle Störfaktoren kontrolliert werden. Im Gegensatz dazu trat späte Aktivität (>300 ms), die auf die Verarbeitung in frontoparietalen Netzwerken hindeutet, unabhängig von der Reizwahrnehmung auf, und Aktivität im anterioren insulären, anterioren cingulären und supplementär-motorischen Kortex war mit der Verarbeitung von Detektionsunsicherheit und der Berichterstattung verbunden. Diese Ergebnisse liefern neue Erkenntnisse zur Debatte um die Relevanz früher, lokaler vs. später, globaler Hirnaktivität und unterstützen die Ansicht, dass perzeptuelles Bewusstsein in modalitätsspezifischen sensorischen Kortizes entsteht

Neural surprise in somatosensory Bayesian learning

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms

The role of predictive processing in conscious access and regularity learning across sensory domains

To increase fitness for survival, organisms not only passively react to environmental changes but also actively predict future events to prepare for potential hazards within their environment. Accumulating evidence indicates that the human brain is a remarkable predictive machine which constantly models causal relationships and predicts future events. This ‘predictive processing’ framework, a prediction-based form of Bayesian inference, states that the brain continuously generates and updates predictions about incoming sensory signals. This framework has been showing notable explanatory power in understanding the mechanisms behind both human behaviour and neurophysiological data and elegantly specifies the underlying computational principles of the neural system. However, even though predictive processing has the potential to provide a unified theory of the brain (Karl Friston, 2010), we still have a limited understanding about fundamental aspects of this model, such as how it deals with different types of information, learns statistical regularities and perhaps most fundamentally of all what its relationship to conscious experience is. This thesis aims to investigate the major gaps in our current understanding of the predictive processing framework via a series of studies. Study 1 investigated the fundamental relationship between unconscious statistical inference reflected by predictive processing and conscious access. It demonstrated that predictions that are in line with sensory evidence accelerate conscious access. Study 2 investigated how low level information within the sensory hierarchy is dealt with by predictive processing and regularity learning mechanisms through “perceptual echo” in which the cross-correlation between a sequence of randomly fluctuating luminance values and occipital electrophysiological (EEG) signals exhibits a long-lasting periodic (~100ms cycle) reverberation of the input stimulus. This study identified a new form of regularity learning and the results demonstrate that the perceptual echo may reflect an iterative learning process, governed by predictive processing. Study 3 investigated how supra-modal predictive processing is capable of learning regularities of temporal duration and also temporal predictions about future events. This study revealed a supramodal temporal prediction mechanism which processes auditory and visual temporal information and integrates information from the duration and rhythmic structures of events. Together these studies provide a global picture of predictive processing and regularity learning across differing types of predictive information

Neural Network Dynamics of Visual Processing in the Higher-Order Visual System

Vision is one of the most important human senses that facilitate rich interaction with the external environment. For example, optimal spatial localization and subsequent motor contact with a specific physical object amongst others requires a combination of visual attention, discrimination, and sensory-motor coordination. The mammalian brain has evolved to elegantly solve this problem of transforming visual input into an efficient motor output to interact with an object of interest. The frontal and parietal cortices are two higher-order (i.e. processes information beyond simple sensory transformations) brain areas that are intimately involved in assessing how an animal’s internal state or prior experiences should influence cognitive-behavioral output. It is well known that activity within each region and functional interactions between both regions are correlated with visual attention, decision-making, and memory performance. Therefore, it is not surprising that impairment in the fronto-parietal circuit is often observed in many psychiatric disorders. Network- and circuit-level fronto-parietal involvement in sensory-based behavior is well studied; however, comparatively less is known about how single neuron activity in each of these areas can give rise to such macroscopic activity. The goal of the studies in this dissertation is to address this gap in knowledge through simultaneous recordings of cellular and population activity during sensory processing and behavioral paradigms. Together, the combined narrative builds on several themes in neuroscience: variability of single cell function, population-level encoding of stimulus properties, and state and context-dependent neural dynamics.Doctor of Philosoph

Utilizing Visual Attention and Inclination to Facilitate Brain-Computer Interface Design in an Amyotrophic Lateral Sclerosis Sample

Individuals who suffer from amyotrophic lateral sclerosis (ALS) have a loss of motor control and possibly the loss of speech. A brain-computer interface (BCI) provides a means for communication through nonmuscular control. Visual BCIs have shown the highest potential when compared to other modalities; nonetheless, visual attention concepts are largely ignored during the development of BCI paradigms. Additionally, individual performance differences and personal preference are not considered in paradigm development. The traditional method to discover the best paradigm for the individual user is trial and error. Visual attention research and personal preference provide the building blocks and guidelines to develop a successful paradigm. This study is an examination of a BCI-based visual attention assessment in an ALS sample. This assessment takes into account the individual’s visual attention characteristics, performance, and personal preference to select a paradigm. The resulting paradigm is optimized to the individual and then tested online against the traditional row-column paradigm. The optimal paradigm had superior performance and preference scores over row-column. These results show that the BCI needs to be calibrated to individual differences in order to obtain the best paradigm for an end user

Continuous sensory-motor transformation and their electrophysiological signatures

Perceptual decisions require efficient transformation of sensory information to motor responses. Most laboratory-based research on decision-making consid- ered discrete and over-simplified actions. This thesis focused on human perfor- mance and electrophysiological signatures of continuous actions in response to decisions from three aspects. First, a systematic comparison between joystick movements and key presses showed that behavioural performance and under- lying cognitive processes are not affected by response modality, establishing the validity and consistency of using joystick trajectories to measure decision responses. Second, a behavioural paradigm was developed to integrate continu- ous circular joystick movements with perceptual decisions of coherent motion. The signal-to-noise ratio of sensory inputs has been shown to affect the ac- curacy and response time of ongoing actions, but its influence on movement speed diminished after substantial training. Multivariate pattern analysis on magnetoecephalography (MEG) data recorded during the experiment identi- fied stable information representations that sensitive to the quality of sensory information as well as the direction of periodic kinematics of circular move- ments. Furthermore, pattern information of complex actions was observed prior to movement onset, indicating the encoding of abstract preparatory ac- tion plans. Third, this thesis investigated the MEG signatures of circular joystick movements initiated via voluntary choices, instead of external sensory inputs. In a novel oddball paradigm, voluntarily choosing a continuous action built up an expectation of the statistical regularity of subsequent sensory in- puts. Violating that expectation via incongruent sensory information resulted in significant multivariate representation in MEG activity of the mismatch event. Overall results presented in this thesis highlighted how ongoing actions can be influenced by, and impact on, the continuous processing of sensory inputs in the human brai