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Background: Temporal principal component analysis (tPCA) has been widely used to extract event-related potentials (ERPs) at group level of multiple subjects ERP data and it assumes that the underlying factor loading is fixed across participants. However, such assumption may fail to work if latency and phase for one ERP vary considerably across participants. Furthermore, effect of number of trials on tPCA decomposition has not been systematically examined as well, especially for within-subject PCA. New method: We reanalyzed a real ERP data of an emotional experiment using tPCA to extract N2 and P2 from single-trial EEG of an individual. We also explored influence of the number of trials (consecutively increased from 10 to 42 trials) on PCA decomposition by comparing temporal correlation, the statistical result, Cronbach’s alpha, spatial correlation of both N2 and P2 for the proposed method with the conventional time-domain analysis, trial-averaged group PCA, and single-trial-based group PCA. Results: The results of the proposed method can enhance spatial and temporal consistency. We could obtain stable N2 with few trials (about 20) for the proposed method, but, for P2, approximately 30 trials were needed for all methods. Comparison with Existing Method(s): About 30 trials for N2 were required and the reconstructed P2 and N2 were poor correlated across participants for the other three methods. Conclusion: The proposed approach may efficiently capture variability of one ERP from an individual that cannot be extracted by group PCA analysis. peerReviewed

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.

Significance Evolution has shaped the human brain as a “single-trial” processor reacting fast and reliably to environmental threats. The noninvasive analysis of its electrical activity commonly draws on electroencephalography/magnetoencephalography (EEG/MEG) with a millisecond time resolution suitable even for short-lived neuronal output spikes. However, while standard EEG/MEG can detect very small evoked population spikes, it does so only after averaging over hundreds of trials. Here, MEG based on an ultralow-noise superconducting quantum interference device (SQUID) is shown to enable the first noninvasive single-trial characterization of human cortical population spikes, revealing amplitudes highly variable between trials and intertrial correlated response latencies. Thus, by expanding the scope of noninvasive neurotechnology, ultralow-noise MEG can support single-trial analyses of input–output characteristics in the human brain., Neuronal spiking is commonly recorded by invasive sharp microelectrodes, whereas standard noninvasive macroapproaches (e.g., electroencephalography [EEG] and magnetoencephalography [MEG]) predominantly represent mass postsynaptic potentials. A notable exception are low-amplitude high-frequency (∼600 Hz) somatosensory EEG/MEG responses that can represent population spikes when averaged over hundreds of trials to raise the signal-to-noise ratio. Here, a recent leap in MEG technology—featuring a factor 10 reduction in white noise level compared with standard systems—is leveraged to establish an effective single-trial portrayal of evoked cortical population spike bursts in healthy human subjects. This time-resolved approach proved instrumental in revealing a significant trial-to-trial variability of burst amplitudes as well as time-correlated (∼10 s) fluctuations of burst response latencies. Thus, ultralow-noise MEG enables noninvasive single-trial analyses of human cortical population spikes concurrent with low-frequency mass postsynaptic activity and thereby could comprehensively characterize cortical processing, potentially also in diseases not amenable to invasive microelectrode recordings.

International audience; When analyzing brain activity such as local field potentials (LFP), it is often desired to represent neural events by stereotypic waveforms. Due to the non-deterministic nature of the neural responses, an adequate waveform estimate typically requires to record multiple repetitions of the neural events. It is common practice to segment the recorded signal into event-related epochs and calculate their average. This approach suffers from two major drawbacks: (i) epoching can be problematic, especially in the case of overlapping neural events and (ii) variability of the neural events across epochs (such as varying onset latencies) is not accounted for, which may lead to a distorted average. In this paper, we propose a novel method called adaptive waveform learning (AWL). It is designed to learn multi-component representations of neural events while explicitly capturing and compensating for waveform variability, such as changing latencies or more general shape variations. Thanks to its generality, it can be applied to both epoched (i.e., segmented) and continuous (i.e., non-epoched) signals by making the corresponding specializations to the algorithm. We evaluate AWL's performance and robustness to noise on simulated data and demonstrate its empirical utility on an electrophysiological recording containing intracranial epileptiform discharges (epileptic spikes).

When, during language processing, a reader or listener is confronted with a structurally deviant phrase, this typically elicits a late positive ERP deflection (P600). The P600 is often understood as a correlate of structural analysis. This assumption has informed a number of neurocognitive models of language. However, the P600 strongly resembles the P3, likely a more general electrophysiological correlate of reorientation behaviour supported by noradrenergic input to the ventral attention network/VAN. Some researchers have proposed that the P600 is an instance of the P3, not a distinct component reflecting the analysis of structured inputs. Here, we tested the P600-as-P3 hypothesis by estimating the alignment of the P600 elicited in a visual sentence processing task to simultaneously collected behavioural measures. A similar analysis was undertaken for a P3 elicited in a separate non-linguistic (face detection) task. Since the P3 is usually aligned to reaction time/RT, the same should hold for the P600; a failure to find RT alignment of the P600 would pose a problem for the P600-as-P3 hypothesis. In contrast, RT alignment of the P600 would associate it with the well-established VAN/Locus Coeruleus e Noradrenaline e Network subserving cortical reorientation.We failed to falsify the hypothesis of RT alignment. Secondary measures, while less unambiguous, were more in agreement with the P600-as-P3 hypothesis. We interpret our results as corroborating the hypothesis that the P600 is a P3, in that it shows that the P600is RT-aligned. This perspective is unpredicted by an account of the P600 as indexing high level processing. Refereed/Peer-reviewed

The probability of an event's occurrence affects event-related potentials (ERPs) on electroencephalograms. The relation between probability and potentials has been discussed by using a quantity called surprise that represents the self-information that humans receive from the event. Previous studies have estimated surprise based on the probability distribution in a stationary state. Our hypothesis is that state transitions also play an important role in the estimation of surprise. In this study, we compare the effects of surprise on the ERPs based on two models that generate an event sequence: a model of a stationary state and a model with state transitions. To compare these effects, we generate the event sequences with Markov chains to avoid a situation that the state transition probability converges with the stationary probability by the accumulation of the event observations. Our trial-by-trial model-based analysis showed that the stationary probability better explains the P3b component and the state transition probability better explains the P3a component. The effect on P3a suggests that the internal model, which is constantly and automatically generated by the human brain to estimate the probability distribution of the events, approximates the model with state transitions because Bayesian surprise, which represents the degree of updating of the internal model, is highly reflected in P3a. The global effect reflected in P3b, however, may not be related to the internal model because P3b depends on the stationary probability distribution. The results suggest that an internal model can represent state transitions and the global effect is generated by a different mechanism than the one for forming the internal model.

Converging evidence from different functional imaging studies indicates that the intensity of activation of different nociceptive areas (including the operculoinsular cortex, the primary somatosensory cortex, and the anterior cingulate gyrus) correlates with perceived pain intensity in the human brain. Brief radiant laser pulses excite selectively Adelta and C nociceptors in the superficial skin layers, provide a purely nociceptive input, and evoke brain potentials (laser-evoked potentials, LEPs) that are commonly used to assess nociceptive pathways in physiological and clinical studies. Adelta-related LEPs are constituted of different components. The earliest is a lateralised, small negative component (N1) which could be generated by the operculoinsular cortex. The major negative component (N2) seems to be mainly the result of activation in the bilateral operculoinsular cortices and contralateral primary somatosensory cortex, and it is followed by a positive component (P2) probably generated by the cingulate gyrus. Currently, early and late LEP components are considered to be differentially sensitive to the subjective variability of pain perception: the late N2-P2 complex strongly correlates with perceived pain, whereas the early N1 component is thought to be a pre-perceptual sensory response. To obtain physiological information on the roles of the pain-related brain areas in healthy humans, we examined the relationship between perceived pain intensity and latency and amplitude of the early (N1) and late (N2, P2) LEP components. We found that the amplitude of the N1 component correlated significantly with the subjective pain ratings, both within and between subjects. Furthermore, we showed that the N2 and P2 late LEP components are differentially sensitive to the perceived sensation, and demonstrated that the N2 component mainly explains the previously described correlation between perceived pain and the amplitude of the N2-P2 vertex complex of LEPs. Our findings confirm the notion that pain intensity processing is distributed over several brain areas, and suggest that the intensity coding of a noxious stimulus occurs already at the earliest stage of perception processing, in the operculoinsular region and, possibly, the primary somatosensory area.

Brain computer interaction (BCI) technologies have proven effective in utilizing single-trial classification algorithms to detect target images in rapid serial visualization presentation tasks. While many factors contribute to the accuracy of these algorithms, a critical aspect that is often overlooked concerns the feature similarity between target and non-target images. In most real-world environments there are likely to be many shared features between targets and non-targets resulting in similar neural activity between the two classes. It is unknown how current neural-based target classification algorithms perform when qualitatively similar target and non-target images are presented. This study address this question by comparing behavioral and neural classification performance across two conditions: first, when targets were the only infrequent stimulus presented amongst frequent background distracters; and second when targets were presented together with infrequent non-targets containing similar visual features to the targets. The resulting findings show that behavior is slower and less accurate when targets are presented together with similar non-targets; moreover, single-trial classification yielded high levels of misclassification when infrequent non-targets are included. Furthermore, we present an approach to mitigate the image misclassification. We use confidence measures to assess the quality of single-trial classification, and demonstrate that a system in which low confidence trials are reclassified through a secondary process can result in improved performance.

The P600, a late positive ERP component following linguistically deviant stimuli, is commonly seen as indexing structural, high-level processes, e.g. of linguistic (re)analysis. It has also been identified with the P3 (P600-as-P3 hypothesis), which is thought to reflect a systemic neuromodulator release facilitating behavioural shifts and is usually response time aligned. We investigated single-trial alignment of the P600 to response, a critical prediction of the P600-as-P3 hypothesis. Participants heard sentences containing morphosyntactic and semantic violations and responded via a button press. The elicited P600 was perfectly response aligned, while an N400 following semantic deviations was stimulus aligned. This is, to our knowledge, the first single-trial analysis of language processing data using within-sentence behavioural responses as temporal covariates. Results support the P600-as-P3 perspective and thus constitute a step towards a neurophysiological grounding of language-related ERPs. Refereed/Peer-reviewed

A key objective in systems and cognitive neuroscience is to establish associations between behavioral measures and concurrent neuronal activity. Single-trial analysis has been proposed as a novel method for characterizing such correlates by first extracting neural components that maximally discriminate trials on a categorical variable, (e.g., hard vs. easy, correct vs. incorrect etc.), and then correlate those components to a continues dependent variable of interest, e.g. reaction time, difficulty Index, etc. However, often times in experiment design it is difficult to either define meaningful categorical variables, or to record enough trials for the method to extract the discriminant components. Experiments designed for the study of the effects of stimulus presentation modality in working memory provide such a scenario, as will be exemplified. In this paper, we proposed a new approach to single-trial analysis in which we directly extract neural activity that maximally correlates to single-trial manual response times; eliminating the need to define an arbitrary categorical variable. We demonstrate our method on real EEG data recordings from the study of stimulus presentation modality effect. © 2013 Christoforou, Constantinidou, Shoshilou and Simos. FEB Cited By :1

Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies, i.e., invariant coordinated activations of groups of muscles, have been proposed as building blocks that the central nervous system (CNS) uses to construct the patterns of muscle activity utilized for executing movements . Several efficient dimensionality reduction algorithms that extract putative synergies from electromyographic (EMG) signals have been developed. Typically, the quality of synergy decompositions is assessed by computing the Variance Accounted For (VAF). Yet, little is known about the extent to which the combination of those synergies en codes task discriminating variations of muscle activity in individual trials. To address this question, here we conceive and develop an ovel computational framework to evaluate muscle synergy decompositions in task space. Unlike previous methods considering the total variance of muscle patterns (VAF based metrics), our approach focuses on variance discriminating execution of different tasks. The procedure is based on single-trial task decoding from muscle synergy activation features. The task decoding based metric evaluates quantitatively the mapping between synergy recruitment and task identification and automatically determines the minimal number of synergies that captures all the task discriminating variability in the synergy activations. In this paper, we first validate the method on plausibly simulated EMG datasets. We then show that it can be applied to different types of muscle synergy decomposition and illustrate its applicability to real data by using it for the analysis of EMG recordings during an arm pointing task. We find that time varying and synchronous synergies with similar number of parameters are equally efficient in task decoding, suggesting that in this experimental paradigm they are equally valid representations of muscle synergies. Overall, these findings stress the effectiveness of the decoding metric in systematically assessing muscle synergy decompositions in task space.''

Mismatch negativity (MMN) is measured by subtracting the averaged response to a set of standard stimuli from the averaged response to rarer deviant stimuli, and taking the amplitude of this difference wave in a given time window. This method is problematic when used to evaluate individuals, because there is no estimate of variance. We describe a new approach, in which independent components with high trial-by-trial variance are first removed. Next, each deviant response has the preceding standard response subtracted, giving a set of single trial difference waves. We illustrate this approach in analysis of MMN to brief tones in 17 adults. The best criterion for MMN combined t-test with an index of inter-trial coherence, giving significant MMN in 14 (82%) of individuals. Single-trial methods can indicate which people show MMN. However, in some clinically normal individuals there was no MMN, despite good behavioral discrimination of stimuli.

Aim: To explore the possible changes in the parameters of the P3 event-related potential (ERP) component among groups of young and older healthy subjects characterized as either high- or low-performers in a visual attention task. Methods: Both conventional and single-trial analyses of the visual P3 component were performed on each group of subjects. Results: P3 component significantly increased in latency as a function of age. The high-performing older subjects showed the posterior predominance of P3, as in young subjects. However, the low-performing older subjects showed a significant P3 amplitude reduction at posterior locations and topographically more widespread activity. Furthermore, single-trial analysis showed that low-performing older subjects presented higher intertrial variability in P3 latency, few trials with P3 generation, and a reduced P3 amplitude in these trials in whom P3 was generated. Conclusion: These data suggest a specific decline in visual target processing in the low-performing older subjects, which would imply a reduction in these attentional brain resources that are allocated to correctly select the relevant stimuli. The implications of this finding for the actual compensation versus dedifferentiation debate in normal aging are discussed But: Comparer les paramètres du composant P3 des potentiels évoqués dans un groupe de sujets jeunes en bonne santé et dans un groupe de sujets âgés classifiés en fonction de leur niveau d’exécution d’une tâche d’attention visuelle (meilleurs et moins bons exécuteurs). Méthodes: Des analyses conventionnelles et des analyses « en sweep unique » du composant P3 ont été réalisées dans chaque groupe de sujets. Résultats: Le temps de latence du composant P3 augmente de façon significative avec l’âge. Chez les sujets âgés meilleurs exécuteurs le P3 prédominait au niveau des régions postérieures, comme chez les sujets jeunes. Par contre, chez les sujets âgés moins bons exécuteurs, le P3 était significativement moins ample en postérieur et plus diffusément réparti sur le scalp. Les analyses « en sweep unique » ont montré que ces derniers présentaient une plus grande variabilité interessai en ce qui concerne le temps de latence de P3, moins de tests où le P3 était présent, ainsi qu’une réduction de l’amplitude de P3 dans les tests où il était présent. Conclusion: Ces données suggèrent l’existence d’un déficit du traitement visuel des stimuli chez les sujets âgés moins bons exécuteurs qui pourrait consister en une réduction des ressources cérébrales attentionnelles mobilisées pour sélectionner correctement le stimulus approprié. Nous discutons les implications de ces résultats dans le débat concernant la redistribution des aires corticales actives chez les sujets âgés (hypothèses de compensation versus dédifférentiation) This research was supported by grants from the Spanish MEC (SEJ2004-01377) and Xunta de Galicia (PGIDT05PXIC21101PN) SI

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.

To make accurate perceptual estimates observers must take the reliability of sensory information into account. Despite many behavioural studies showing that subjects weight individual sensory cues in proportion to their reliabilities, it is still unclear when during a trial neuronal responses are modulated by the reliability of sensory information, or when they reflect the perceptual weights attributed to each sensory input during decision making. We investigated these questions using a combination of psychophysics, EEG based neuroimaging and single-trial decoding. Our results show that the weighted integration of sensory information in the brain is a dynamic process; effects of sensory reliability on task-relevant EEG components were evident around 84ms after stimulus onset, while neural correlates of perceptual weights emerged around 120ms after stimulus onset. These neural processes also had different underlying topographies, arising from areas consistent with sensory and parietal regions. Together these results reveal the temporal dynamics of perceptual and neural audio-visual integration and support the notion of temporally early and functionally specific multisensory processes in the brain.