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Theories of the electrophysiology of language comprehension are mostly informed by event-related potential effects observed between condition averages. We here argue that a dissociation between competing effect-level explanations of event-related potentials can be achieved by turning to predictions and analyses at the single-trial level. Specifically, we examine the single-trial dynamics in event-related potential data that exhibited a biphasic N400–P600 effect pattern. A group of multi-stream models can explain biphasic effects by positing that each individual trial should induce either an N400 increase or a P600 increase, but not both. An alternative, single-stream account, Retrieval-Integration theory, explicitly predicts that N400 amplitude and P600 amplitude should be correlated at the single-trial level. In order to investigate the single-trial dynamics of the N400 and the P600, we apply a regression-based technique in which we quantify the extent to which N400 amplitudes are predictive of the electroencephalogram in the P600 time window. Our findings suggest that, indeed, N400 amplitudes and P600 amplitudes are inversely correlated within-trial and, hence, the N400 effect and the P600 effect in biphasic data are driven by the same trials. Critically, we demonstrate that this finding also extends to data which exhibited only monophasic effects between conditions. In sum, the observation that the N400 is inversely correlated with the P600 on a by-trial basis supports a single stream view, such as Retrieval-Integration theory, and is difficult to reconcile with the processing mechanisms proposed by multi-stream models. [ABSTRACT FROM AUTHOR]

Introduction: One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. Methods: We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Results: Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the secondM20 forMEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in di [ABSTRACT FROM AUTHOR]

We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in F1-score. [ABSTRACT FROM AUTHOR]

Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to-noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90° out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into low-dimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robot-assisted bimanual training.

Methylmercury pollution is a global problem, and Minamata disease (MD) is a stark reminder that exposure to methylmercury can cause irreversible neurological damage. A “glove and stocking type” sensory disturbance due to injured primary sensory cortex (SI) (central somatosensory disturbance) is the most common neurologic sign in MD. As this sign is also prevalent in those with polyneuropathy, we aimed to develop an objective assessment for detecting central somatosensory disturbances in cases of chronic MD.We selected 289 healthy volunteers and 42 patients with MD. We recorded the sensory nerve action potentials (SNAPs) and somatosensory evoked magnetic fields (SEFs) to median nerve stimulation with magnetoencephalography. Single-trial epochs were classified into three categories (N20m, non-response, and P20m epochs) based on the cross-correlation between averaged sensor SEFs and individual epochs. We assessed SI responses (the appearance rate of P20m [P20m rate] and non-response epochs [non-response rate]) and early somatosensory cortical processing (N20m amplitude, reproducibility of N20m in single-trial responses [cross-correlation value], and induced gamma-band oscillations of the SI [gamma response] of single epochs excluding non-response epochs). Receiver operating characteristic curve analyses were used to examine the diagnostic accuracy of each parameter.We found that SNAPs exerted a marginal effect on the N20m. The N20m amplitude, cross-correlation value, and gamma response were significantly reduced in the MD group on either side (p 0.77 (range: 0.77–0.79) for all parameters. Their confidence intervals overlapped with each other; thus, each SEF parameter likely had an approximately equivalent discrimination ability.In conclusion, chronic MD is characterized by impaired SI responses and alterations in early somatosensory cortical processing. Thus, single-trial neuromagnetic analysis of somatosensory function may be useful for detecting central somatosensory disturbance and elucidating the relevant pathophysiological mechanisms even in the context of chronic MD.

Several recent studies investigated the rhythmic nature of cognitive processes that lead to perception and behavioral report. These studies used different methods, and there has not yet been an agreement on a general standard. Here, we present a way to test and quantitatively compare these methods. We simulated behavioral data from a typical experiment and analyzed these data with several methods. We applied the main methods found in the literature, namely sine-wave fitting, the discrete Fourier transform (DFT) and the least square spectrum (LSS). DFT and LSS can be applied both on the average accuracy time course and on single trials. LSS is mathematically equivalent to DFT in the case of regular, but not irregular sampling - which is more common. LSS additionally offers the possibility to take into account a weighting factor which affects the strength of the rhythm, such as arousal. Statistical inferences were done either on the investigated sample (fixed-effects) or on the population (random-effects) of simulated participants. Multiple comparisons across frequencies were corrected using False Discovery Rate, Bonferroni, or the Max-Based approach. To perform a quantitative comparison, we calculated sensitivity, specificity and D-prime of the investigated analysis methods and statistical approaches. Within the investigated parameter range, single-trial methods had higher sensitivity and D-prime than the methods based on the average accuracy time course. This effect was further increased for a simulated rhythm of higher frequency. If an additional (observable) factor influenced detection performance, adding this factor as weight in the LSS further improved sensitivity and D-prime. For multiple comparison correction, the Max-Based approach provided the highest specificity and D-prime, closely followed by the Bonferroni approach. Given a fixed total amount of trials, the random-effects approach had higher D-prime when trials were distributed over a larger number of participants, even though this gave less trials per participant. Finally, we present the idea of using a dampened sinusoidal oscillator instead of a simple sinusoidal function, to further improve the fit to behavioral rhythmicity observed after a reset event.

[Display omitted] • Autistic children have difficulties processing emotion-inhibition interactions. • Single-trial analysis identified ERPs and neural variability patterns. • Autistic children showed altered neural mechanisms during emotion-inhibition interaction. This study investigated the neural dynamics underlying the interplay between emotion and inhibition in Chinese autistic children. Electroencephalography (EEG) signals were recorded from 50 autistic and 46 non-autistic children during an emotional Go/Nogo task. Based on single-trial ERP analyses, autistic children, compared to their non-autistic peers, showed a larger Nogo-N170 for angry faces and an increased Nogo-N170 amplitude variation for happy faces during early visual perception. They also displayed a smaller N200 for all faces and a diminished Nogo-N200 amplitude variation for happy and neutral faces during inhibition monitoring and preparation. During the late stage, autistic children showed a larger posterior-Go-P300 for angry faces and an augmented posterior-Nogo-P300 for happy and neutral faces. These findings clarify the differences in neural processing of emotional stimuli and inhibition between Chinese autistic and non-autistic children, highlighting the importance of considering these dynamics when designing intervention to improve emotion regulation in autistic children. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Robot-assisted bimanual training is promising to improve motor function and cortical reorganization for hemiparetic stroke patients. Closing the rehabilitation training loop with neurofeedback can help refine training protocols in time for better engagements and outcomes. However, due to the low signal-to-noise ratio (SNR) and non-stationary properties of neural signals, reliable characterization of bimanual training-induced neural activities from single-trial measurement is challenging. In this study, ten human participants were recruited conducting robot-assisted bimanual cyclical tasks (in-phase, 90° out-of-phase, and anti-phase) when concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were recorded. A unified EEG-fNIRS bimodal signal processing framework was proposed to characterize neural activities induced by three types of bimanual cyclical tasks. In this framework, novel artifact removal methods were used to improve the SNR and the task-related component analysis (TRCA) was introduced to increase the reproducibility of EEG-fNIRS bimodal features. The optimized features were transformed into low-dimensional indicators to reliably characterize bimanual training-induced neural activation. The SVM classification results of three bimanual cyclical tasks revealed a good discrimination ability of EEG-fNIRS bimodal indicators (90.1%), which was higher than that using EEG (74.8%) or fNIRS (82.2%) alone, supporting the proposed method as a feasible technique to characterize neural activities during robot-assisted bimanual training. [ABSTRACT FROM AUTHOR]

Intra-individual variability (IIV), a measure of variance within an individual's performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-trial level is challenging. Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and trial-to-trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-trial classification pipeline was developed to detect the changes of mental fatigue levels. We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%. The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue. This study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline. • The modulations of mental fatigue on IIV were studied at the behavioral and neural levels. • IIV at the behavioral level was measured by standard deviations of RT across trials, • RIDE was used to quantify IIV from single-trial ERP and reconstruct latency-corrected ERP. • Temporal PCA was applied for comparison between raw ERP and reconstructed ERP. • A single-trial classification pipeline was developed for mental fatigue detection. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The frontal cortex-striatum circuit plays a pivotal role in adaptive goal-directed behaviors. However, it remains unclear how decision-related signals are mediated through cross-regional transmission between the medial frontal cortex and the striatum by neuronal ensembles in making decision based on outcomes of past action. Here, we analyzed neuronal ensemble activity obtained through simultaneous multiunit recordings in the secondary motor cortex (M2) and dorsal striatum (DS) in rats performing an outcome-based left-or-right choice task. By adopting tensor component analysis (TCA), a single-trial-based unsupervised dimensionality reduction approach, for concatenated ensembles of M2 and DS neurons, we identified distinct three spatiotemporal neural dynamics (TCA components) at the single-trial level specific to task-relevant variables. Choice-position-selective neural dynamics reflected the positions chosen and was correlated with the trial-to-trial fluctuation of behavioral variables. Intriguingly, choice-pattern-selective neural dynamics distinguished whether the incoming choice was a repetition or a switch from the previous choice before a response choice. Other neural dynamics was selective to outcome and increased within-trial activity following response. Our results demonstrate how the concatenated ensembles of M2 and DS process distinct features of decision-related signals at various points in time. Thereby, the M2 and DS collaboratively monitor action outcomes and determine the subsequent choice, whether to repeat or switch, for action selection., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Handa et al.)

To explore the issue of how the human brain processes sentences with different levels of complexity, we sought to compare the neural substrates underlying the processing of Chinese subject-extracted relative clause (SRC) and object-extracted relative clause (ORC) sentences in a trial-by-trial fashion. Previous neuroimaging studies have demonstrated that the involvement of the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) is critical for the processing of relative clause (RC) sentences. In this study, we employed independent component analysis (ICA) to decompose brain activity into a set of independent components. Then, the independent component maps were spatially normalized using a surface-based approach in order to further spatially correlate and match the equivalent components from individual participants. The selected equivalent components indicated that the LIFG and the LSTG were consistently engaged in sentence processing among the participants. Subsequently, we observed alterations in the functional coupling between the LIFG and the LSTG in response to SRCs and ORCs using a Granger causality analysis. Specifically, comprehending Chinese ORCs with a canonical word order only involved a unidirectional connection from the LIFG to the LSTG for the integration of lexical-syntactic information. On the other hand, comprehending Chinese SRCs required bi-directional connectivity between the LIFG and the LSTG to fulfill increased integration demands in reconstructing the argument hierarchy due to a non-canonical word order. Furthermore, through a single-trial analysis, the strength of the connectivity from the LIFG to the LSTG was found to be significantly correlated with the complexity of the SRC sentences as quantified by eye-tracking measures. These findings indicated that the effective connectivity from the LIFG to the LSTG played an important role in the comprehension of complex sentences and that enhanced strength of this connectivity might reflect increased integration demands and restructuring attempts during sentence processing. Taken together, the results of the present study reveal that interregional interaction in the brain network for sentence processing can be dynamically engaged in response to different levels of complexity and also shed some light on the interpretation of neuroimaging and behavioral evidence when accounting for the nature of sentence complexity during reading.

A relative indifference to the human voice is a characteristic of Autism Spectrum Disorder (ASD). Yet, studies of voice perception in ASD provided contradictory results: one study described an absence of preferential response to voices in ASD while another reported a larger activation to vocal sounds than environmental sounds, as seen in typically developed (TD) adults. In children with ASD, an absence of preferential response to vocal sounds was attributed to an atypical response to environmental sounds. To have a better understanding of these contradictions, we re-analyzed the data from sixteen children with ASD and sixteen age-matched TD children to evaluate both inter- and intra-subject variability. Intra-subject variability was estimated with a single-trial analysis of electroencephalographic data, through a measure of inter-trial consistency, which is the proportion of trials showing a positive activity in response to vocal and non-vocal sounds. Results demonstrate a larger inter-subject variability in response to non-vocal sounds, driven by a subset of children with ASD (7/16) who do not show the expected negative Tb peak in response to non-vocal sounds around 200 ms after the start of the stimulation due to a reduced inter-trial consistency. A logistic regression model with age and clinical parameters allowed demonstrating that not a single parameter discriminated the subgroups of ASD participants. Yet, the electrophysiologically-based groups differed on a linear combination of parameters. Children with ASD showing a reduced inter-trial consistency were younger and characterized by lower verbal developmental quotient and less attempt to communicate by voice. This data suggests that a lack of specialization for processing social signal may stem from an atypical processing of environmental sounds, linked to the development of general communication abilities. Discrepancy reported in the literature may arise from that heterogeneity and it may be inadequate to divide children with ASD based only on intellectual quotient or language abilities. This analysis could be a useful tool in providing complementary information for the functional diagnostic of ASD and evaluating verbal communication impairment.

We propose an approach for the detection of language expectation violations that occur in communication. We examined semantic and syntactic violations from electroencephalogram (EEG) when participants listened to spoken sentences. Previous studies have shown that such event-related potential (ERP) components as N400 and the late positivity (P600) are evoked in the auditory where semantic and syntactic anomalies occur. We used this knowledge to detect language expectation violation from single-trial EEGs by machine learning techniques. We recorded the brain activity of 18 participants while they listened to sentences that contained semantic and syntactic anomalies and identified the significant main effects of these anomalies in the ERP components. We also found that a multilayer perceptron achieved 59.5% (semantic) and 57.7% (syntactic) accuracies.

A relative indifference to the human voice is a characteristic of Autism Spectrum Disorder (ASD). Yet, studies of voice perception in ASD provided contradictory results: one study described an absence of preferential response to voices in ASD while another reported a larger activation to vocal sounds than environmental sounds, as seen in typically developed (TD) adults. In children with ASD, an absence of preferential response to vocal sounds was attributed to an atypical response to environmental sounds. To have a better understanding of these contradictions, we re-analyzed the data from sixteen children with ASD and sixteen age-matched TD children to evaluate both inter- and intra-subject variability. Intra-subject variability was estimated with a single-trial analysis of electroencephalographic data, through a measure of inter-trial consistency, which is the proportion of trials showing a positive activity in response to vocal and non-vocal sounds. Results demonstrate a larger inter-subject variability in response to non-vocal sounds, driven by a subset of children with ASD (7/16) who do not show the expected negative Tb peak in response to non-vocal sounds around 200 ms after the start of the stimulation due to a reduced inter-trial consistency. A logistic regression model with age and clinical parameters allowed demonstrating that not a single parameter discriminated the subgroups of ASD participants. Yet, the electrophysiologically-based groups differed on a linear combination of parameters. Children with ASD showing a reduced inter-trial consistency were younger and characterized by lower verbal developmental quotient and less attempt to communicate by voice. This data suggests that a lack of specialization for processing social signal may stem from an atypical processing of environmental sounds, linked to the development of general communication abilities. Discrepancy reported in the literature may arise from that heterogeneity and it may be inadequate to divide children with ASD based only on intellectual quotient or language abilities. This analysis could be a useful tool in providing complementary information for the functional diagnostic of ASD and evaluating verbal communication impairment. [ABSTRACT FROM AUTHOR]

The P300 is a widely studied event-related potential, which allows non-muscular communication. In P300 induced brain–computer interfacing, one often comes across the challenge of modeling uncertainties due to fluctuations in EEG feature values within a specific session and across several sessions of EEG recordings of a specific subject. The relevance of fuzzy systems in this domain thus cannot be undermined. In this paper, the authors propose (a) an interval type-2 fuzzy classifier for detecting P300 occurrences and (b) a feature tuning algorithm for selection of Autoregressive Yule Parameter features of optimal lag-length corresponding to individual electrodes with an aim to maximize a classifier-oriented performance metric. The classifier performance metric is formulated as a simple objective function tailored to the classifier performance in terms of low uncertainty and high classification accuracy. The relationship between the proposed objective function value and classification accuracy is found to be statistically significant over iterations. The experimental results show that the proposed algorithm achieves an average accuracy of 90.8%. [ABSTRACT FROM AUTHOR]

We propose an approach for the detection of language expectation violations that occur in communication. We examined semantic and syntactic violations from electroencephalogram (EEG) when participants listened to spoken sentences. Previous studies have shown that such event-related potential (ERP) components as N400 and the late positivity (P600) are evoked in the auditory where semantic and syntactic anomalies occur. We used this knowledge to detect language expectation violation from single-trial EEGs by machine learning techniques. We recorded the brain activity of 18 participants while they listened to sentences that contained semantic and syntactic anomalies and identified the significant main effects of these anomalies in the ERP components. We also found that a multilayer perceptron achieved 59.5% (semantic) and 57.7% (syntactic) accuracies. [ABSTRACT FROM AUTHOR]

The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice.

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.

Previous studies demonstrated that pain induced by a noxious stimulus during a distraction task is affected by both stimulus-driven and goal-directed processes which interact and change over time. The purpose of this exploratory study was to analyse associations of aspects of subjective pain experience and engagement with the distracting task with attention-sensitive components of noxious laser-evoked potentials (LEPs) on a single-trial basis. A laser heat stimulus was applied to the dorsum of the left hand while subjects either viewed the Rubin vase-face illusion (RVI), or focused on their pain and associated somatosensory sensations occurring on their stimulated hand. Pain-related sensations occurring with every laser stimulus were evaluated using a set of visual analogue scales. Factor analysis was used to identify the principal dimensions of pain experience. LEPs were correlated with subjective aspects of pain experience on a single-trial basis using a multiple linear regression model. A positive LEP component at the vertex electrodes in the interval 294-351 ms (P2) was smaller during focusing on RVI than during focusing on the stimulated hand. Single-trial amplitude variations of the P2 component correlated with changes in Factor 1, representing essential aspects of pain, and inversely with both Factor 2, accounting for anticipated pain, and the number of RVI figure reversals. A source dipole located in the posterior region of the cingulate cortex was the strongest contributor to the attention-related single-trial variations of the P2 component. Instantaneous amplitude variations of the P2 LEP component during switching attention towards pain in the presence of a distracting task are related to the strength of pain experience, engagement with the task, and the level of anticipated pain. Results provide neurophysiological underpinning for the use of distraction analgesia acute pain relief. [ABSTRACT FROM AUTHOR]

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements. [ABSTRACT FROM AUTHOR]

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. 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 84 ms after stimulus onset, while neural correlates of perceptual weights emerged 120 ms after stimulus onset. These neural processes had different underlying sources, arising from sensory and parietal regions, respectively. 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. [ABSTRACT FROM AUTHOR]

When analyzing brain activity such as local field potentials, it is often desired to represent neural events by stereotypic waveforms. Due to the nondeterministic nature of the neural responses, an adequate waveform estimate typically requires recording 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: epoching can be problematic, especially in the case of overlapping neural events, and 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 multicomponent 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., nonepoched) 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). [ABSTRACT FROM PUBLISHER]

Neuroimaging and neuropsychological studies have identified the involvement of the right posterior region in the processing of visual words. Interestingly, in contrast, ERP studies of the N170 typically demonstrate selectivity for words more strikingly over the left hemisphere. Why is right hemisphere selectivity for words during the N170 epoch typically not observed, despite the clear involvement of this region in word processing? One possibility is that amplitude differences measured on averaged ERPs in previous studies may have been obscured by variation in peak latency across trials. This study examined this possibility by using single-trial analysis. Results show that words evoked greater single-trial N170s than control stimuli in the right hemisphere. Additionally, we observed larger trial-to-trial variability on N170 peak latency for words as compared to control stimuli over the right hemisphere. Results demonstrate that, in contrast to much of the prior literature, the N170 can be selective to words over the right hemisphere. This discrepancy is explained in terms of variability in trial-to-trial peak latency for responses to words over the right hemisphere. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We develop a novel methodology for the single-trial analysis of multichannel time-varying neuroimaging signals. We introduce the space-by-time M/EEG decomposition, based on Non-negative Matrix Factorization (NMF), which describes single-trial M/EEG signals using a set of non-negative spatial and temporal components that are linearly combined with signed scalar activation coefficients. We illustrate the effectiveness of the proposed approach on an EEG dataset recorded during the performance of a visual categorization task. Our method extracts three temporal and two spatial functional components achieving a compact yet full representation of the underlying structure, which validates and summarizes succinctly results from previous studies. Furthermore, we introduce a decoding analysis that allows determining the distinct functional role of each component and relating them to experimental conditions and task parameters. In particular, we demonstrate that the presented stimulus and the task difficulty of each trial can be reliably decoded using specific combinations of components from the identified space-by-time representation. When comparing with a sliding-window linear discriminant algorithm, we show that our approach yields more robust decoding performance across participants. Overall, our findings suggest that the proposed space-by-time decomposition is a meaningful low-dimensional representation that carries the relevant information of single-trial M/EEG signals. [ABSTRACT FROM AUTHOR]

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. 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. 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. About 30 trials for N2 were required and the reconstructed P2 and N2 were poor correlated across participants for the other three methods. The proposed approach may efficiently capture variability of one ERP from an individual that cannot be extracted by group PCA analysis. [ABSTRACT FROM AUTHOR]

Objective: We investigated neurophysiological brain responses elicited by a tactile event-related potential paradigm in a sample of ALS patients. Underlying cognitive processes and neurophysiological signatures for brain-computer interface (BCI) are addressed. Methods: We stimulated the palm of the hand in a group of fourteen ALS patients and a control group of ten healthy participants and recorded electroencephalographic signals in eyes-closed condition. Target and non-target brain responses were analyzed and classified offline. Classification errors served as the basis for neurophysiological brain response sub-grouping. Results: A combined behavioral and quantitative neurophysiological analysis of sub-grouped data showed neither significant between-group differences, nor significant correlations between classification performance and the ALS patients' clinical state. Taking sequential effects of stimuli presentation into account, analyses revealed mean classification errors of 19.4% and 24.3% in healthy participants and ALS patients respectively. Conclusions: Neurophysiological correlates of tactile stimuli presentation are not altered by ALS. Tactile event-related potentials can be used to monitor attention level and task performance in ALS and may constitute a viable basis for future BCIs. [ABSTRACT FROM AUTHOR]

Objective: The literature suggests that the N1 and P2 waves of the auditory ERP are dissociable at the developmental, experimental, and source levels. At the experimental level, inconsistent findings suggest different effects of intensity on the amplitudes of the auditory N1 and P2. Our main goal was to analyze the intensity dependence of the auditory N1 and P2 while controlling for habituation effects. Methods: We examined the intensity dependence of both averaged and single-trial auditory N1 and P2 waves elicited in a repeated-stimulation protocol. Results: N1 and P2 revealed similar intensity dependence on both standard and filter denoised ERP, with a linear tendency for higher intensities to elicit higher absolute peak amplitudes. At the single-trial level, both waves covary irrespective of stimulus intensity and trial order. Conclusions: Our results suggest that stimulus intensity variation induces similar effects on both and N1 and P2 and partially contradict previous data that classified the P2 as a non-habituating component. Significance: Our findings contribute to the ongoing discussion on the functional significance of the auditory P2 deflection. In addition, the present work demonstrated the applicability of a filter denoising method for single-trial estimation in the analysis of the experimental effects on auditory ERP components. [ABSTRACT FROM AUTHOR]

Abnormalities of somatosensory evoked potentials (SEPs) provide effective evidence for impairment of the somatosensory system, so that SEPs have been widely used in both clinical diagnosis and intraoperative neurophysiological monitoring. However, due to their low signal-to-noise ratio (SNR), SEPs are generally measured using ensemble averaging across hundreds of trials, thus unavoidably producing a tardiness of SEPs to the potential damages caused by surgical maneuvers and a loss of dynamical information of cortical processing related to somatosensory inputs. Here, we aimed to enhance the SNR of single-trial SEPs using Kalman filtering and time-frequency multiple linear regression (TF-MLR) and measure their single-trial parameters, both in the time domain and in the time-frequency domain. We first showed that, Kalman filtering and TF-MLR can effectively capture the single-trial SEP responses and provide accurate estimates of single-trial SEP parameters in the time domain and time-frequency domain, respectively. Furthermore, we identified significant correlations between the stimulus intensity and a set of indicative single-trial SEP parameters, including the correlation coefficient (between each single-trial SEPs and their average), P37 amplitude, N45 amplitude, P37-N45 amplitude, and phase value (at the zero-crossing points between P37 and N45). Finally, based on each indicative single-trial SEP parameter, we investigated the minimum number of trials required on a single-trial basis to suggest the existence of SEP responses, thus providing important information for fast SEP extraction in intraoperative monitoring. [ABSTRACT FROM AUTHOR]

In lifespan studies, large within-group heterogeneity with regard to behavioral and neuronal data is observed. This casts doubt on the validity of group-statistics-based approaches to understand age-related changes on cognitive and neural levels. Recent progress in brain–computer interface research demonstrates the potential of machine learning techniques to derive reliable person-specific models, representing brain behavior mappings. The present study now proposes a supervised learning approach to derive person-specific models for the identification and quantification of interindividual differences in oscillatory EEG responses related to working memory selection and maintenance mechanisms in a heterogeneous lifespan sample. EEG data were used to discriminate different levels of working memory load and the focus of visual attention. We demonstrate that our approach leads to person-specific models with better discrimination performance compared to classical person-nonspecific models. We show how these models can be interpreted both on an individual as well as on a group level. One of the key findings is that, with regard to the time dimension, the between-person variance of the obtained person-specific models is smaller in older than in younger adults. This is contrary to what we expected because of increased behavioral and neuronal heterogeneity in older adults. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. Methods The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. Results Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz (‘ σ -bursts’) can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz (‘ κ -bursts’) enabling their detection in non-invasive surface EEG. Conclusions sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses. [ABSTRACT FROM AUTHOR]

Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLR d ) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI. [ABSTRACT FROM AUTHOR]

Several studies provided evidence that the amplitudes of laser-evoked potentials (LEPs) are modulated by attention. However, previous reports were based on across-trial averaging of LEP responses at the expense of losing information about intertrial variability related to attentional modulation. The aim of this study was to investigate the effects of somatosensory spatial attention on single-trial parameters (i.e., amplitudes, latencies, and latency jitter) of LEP components (N2 and P2). Twelve subjects participated in a sustained spatial attention paradigm while noxious laser stimuli (left hand) and noxious electrical stimuli (right hand) were sequentially delivered to the dorsum of the respective hand with nonnoxious air puffs randomly interspersed within the sequence of noxious stimuli. Participants were instructed to mentally count all stimuli (i.e., noxious and nonnoxious) applied to the attended location. Laser stimuli, presented to the attended hand (ALS), elicited larger single-trial amplitudes of the N2 component compared with unattended laser stimuli (ULS). In contrast, single-trial amplitudes of the P2 component were not significantly affected by spatial attention. Single-trial latencies of the N2 and P2 were significantly smaller for ALS vs. ULS. Additionally, the across-trial latency jitter of the N2 component was reduced for ALS. Conversely, the latency jitter of the P2 component was smaller for ULS compared with ALS. With the use of single-trial analysis, the study provided new insights into brain dynamics of LEPs related to spatial attention. Our results indicate that single-trial parameters of LEP components are differentially modulated by spatial attention. [ABSTRACT FROM AUTHOR]

Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). In the first part of the paper, we illustrate how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces.

Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9-15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns.

In this study we summarize the features that characterize thepre-movements and pre-motor imageries (before imagining themovement) EEG data in humans from both Neuroscientists andEngineers’ point of view. We demonstrate what the brain status isbefore a voluntary movement and how it has been used in practicalapplications such as brain computer interfaces (BCIs). Usually, inBCI applications, the focus of study is on the after-movement ormotor imagery potentials. However, this study shows that it ispossible to develop BCIs based on the before-movement or motorimagery potentials such as the Bereitschaftspotential. Using the pre-movement or pre-motor imagery potentials, we can correctly predictthe onset of the upcoming movement, its direction and even the limbthat is engaged in the performance. This information can help indesigning a more efficient rehabilitation tool as well as BCIs with ashorter response time which appear more natural to the users.