Search

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity. [ABSTRACT FROM AUTHOR]

Single trial estimation of event‐related potential (ERP) components is an open research topic in neuroscience. In this article, we have proposed a method to improve the performance of spatiotemporal filtering by decreasing its dependency to prior estimates of ERP components. For this purpose, we have used a mixture of Gaussian kernels instead of a raw prior signal, and the parameters of the Gaussian kernel are estimated using artificial bee colony algorithm. The algorithm starts with one Gaussian kernel, and after optimizing its parameters, another Gaussian kernel is added. This procedure goes on until the stopping criterion is reached. The efficiency of the algorithm is tested for one single uncorrelated component and two correlated components for synthesized electroencephalogram (EEG) signal. Also, the efficiency of the proposed method is presented on real data for extraction of N170 component in real EEG data. [ABSTRACT FROM AUTHOR]

Abstract Background We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism. Methods We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines. Results The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p

Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The Long-Range Temporal Correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling of short- and long-range temporal correlations. There were significant (p < 0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in Brain Computer Interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. A significantly higher (p < 0.05) classification accuracy of 88.3 ± 4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted the earliest movement at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement. [ABSTRACT FROM AUTHOR]

Background Although acquisition techniques have improved tremendously, the neuroscientific understanding of complex cognitive phenomena is still incomplete. One of the reasons for this shortcoming may be the lack of sophisticated signal processing methods. Complex cognitive phenomena usually involve various mental subprocesses whose temporal occurrence varies from trial to trial. Mostly, these mental subprocesses require large-scale integration processes between multiple brain areas that are most likely mediated by complex, non-linear phase coupling mechanisms. Consequently, a spatiotemporal analysis of complex, multivariate phase synchronization patterns on a single trial basis is necessary. New method This paper introduces the HEURECA method (How to Evaluate and Uncover Recurring EEG Coupling Arrangements) that enables the dynamic detection of distinguishable multivariate functional connectivity states in the electroencephalogram. HEURECA adaptively divides a trial into segments of quasi-stable phase coupling topographies and assigns similar topographies to the same synchrostate cluster. Results HEURECA is evaluated by means of simulated data. The results show that it reliably reconstructs a time series of recurring phase coupling topographies and successfully gathers them into clusters of interpretable neural synchrostates. The advantages and unique features of HEURECA are further illustrated by investigating the popular complex cognitive phenomenon insight. Comparison with existing methods Unlike existing methods, HEURECA detects complex phase relationships between more than two signals and is applicable to single trials. Conclusions Since HEURECA is applicable to all kinds of circular data, it not only provides new insights into insight, but also into a variety of other phenomena in neuroscience, physics or other scientific fields. [ABSTRACT FROM AUTHOR]

• A compact CNN model with channel-wise attention and dilated temporal convolution is proposed for P300 detection in P300 speller. • Channel-wise attention is incorporated to give more importance to relevant channels and thus reduce channel redundancy. • Dilated convolution is adopted to extract representative temporal features with much smaller number of parameters. • Cost sensitive learning is used to deal with class imbalance and avoid potential biasing towards majority class. • Better tradeoff is obtained between classification performance and computational complexity compared to state-of-the-art models. The P300 speller is a challenging task due to various factors, such as morphological and temporal variabilities, noisy channels, imbalanced P300/non-P300 data, numerous trainable parameters, and lengthy spelling cycles. To overcome these issues, we propose a novel solution that incorporates temporal dilated convolution with channel-wise attention into a lightweight base classifier and employs cost-sensitive learning to handle data imbalance. We evaluate our system through extensive subject-dependent and cross-subject experiments on two standard datasets: Amyotrophic Lateral Sclerosis (ALS) and Devanagari Script (DS). Our results demonstrate a significant improvement in P300 classification with a 2–3 times reduction in trainable parameters compared to single-trial experiments. Our proposed model outperforms state-of-the-art compact models and offers better trade-off between computational complexity and classification performance. Our approach offers new insights into the challenges of P300 classification and provides inspiration for future research in this field. We are confident that our proposed model will contribute to the advancement of P300 classification and benefit the scientific community. [ABSTRACT FROM AUTHOR]

Brain computer interfaces provide a novel channel for the communication between brain and output devices. The effectiveness of the brain computer interface is based on the classification accuracy of single trial brain signals. The common spatial pattern (CSP) algorithm is believed to be an effective algorithm for the classification of single trial brain signals. As the amplitude feature for spatial projection applied by this algorithm is based on a broad frequency bandpass filter (mainly 5–30 Hz) in which the frequency band is often selected by experience, the CSP is sensitive to noise and the influence of other irrelevant information in the selected broad frequency band. In this paper, to improve the CSP, a novel relevant feature integration and extraction algorithm is proposed. Before projecting, we integrated the motor relevant information to suppress the interference of noise and irrelevant information, as well as to improve the spatial difference for projection. The algorithm was evaluated with public datasets. It showed significantly better classification performance with single trial electroencephalography (EEG) data, increasing by 6.8% compared with the CSP.

Background: We aimed to investigate the separability of the neural correlates of 2 types of motor imagery, self and third person (actions owned by the participant himself vs. another individual). If possible this would allow for the development of BCI interfaces to train disorders of action and intention understanding beyond simple imitation, such as autism.Methods: We used EEG recordings from 20 healthy participants, as well as electrocorticography (ECoG) in one, based on a virtual reality setup. To test feasibility of discrimination between each type of imagery at the single trial level, time-frequency and source analysis were performed and further assessed by data-driven statistical classification using Support Vector Machines.Results: The main observed differences between self-other imagery conditions in topographic maps were found in Frontal and Parieto-Occipital regions, in agreement with the presence of 2 independent non μ related contributions in the low alpha frequency range. ECOG corroborated such separability. Source analysis also showed differences near the temporo-parietal junction and single-trial average classification accuracy between both types of motor imagery was 67 ± 1%, and raised above 70% when 3 trials were used. The single-trial classification accuracy was significantly above chance level for all the participants of this study (p < 0.02).Conclusions: The observed pattern of results show that Self and Third Person MI use distinct electrophysiological mechanisms detectable at the scalp (and ECOG) at the single trial level, with separable levels of involvement of the mirror neuron system in different regions. These observations provide a promising step to develop new BCI training/rehabilitation paradigms for patients with neurodevelopmental disorders of action understanding beyond simple imitation, such as autism, who would benefit from training and anticipation of the perceived intention of others as opposed to own intentions in social contexts. [ABSTRACT FROM AUTHOR]

We provide a quantitative assessment of the parallel-processing hypothesis included in various language-processing models. First, we highlight the importance of reasoning about cognitive processing at the level of single trials rather than using averages. Then, we report the results of an experiment in which the hypothesis was tested at an unprecedented level of granularity with intracerebral data recorded during a picture-naming task. We extracted patterns of significant high-gamma activity from multiple patients and combined them into a single analysis framework that identified consistent patterns. Average signals from different brain regions, presumably indexing distinct cognitive processes, revealed a large degree of concurrent activity. In comparison, at the level of single trials, the temporal overlap of detected significant activity was unexpectedly low, with the exception of activity in sensory cortices. Our novel methodology reveals some limits on the degree to which word production involves parallel processing. [ABSTRACT FROM AUTHOR]

Objective. In this paper, we propose a new tensor decomposition to extract event-related potentials (ERP) by adding a physiologically meaningful constraint to the Tucker decomposition. Approach. We analyze the performance of the proposed model and compare it with Tucker decomposition by synthesizing a dataset. The simulated dataset is generated using a 12th-order autoregressive model in combination with independent component analysis (ICA) on real no-task electroencephalogram (EEG) recordings. The dataset is manipulated to contain the P300 ERP component and to cover different SNR conditions, ranging from 0 to -30 dB, to simulate the presence of the P300 component in extremely noisy recordings. Furthermore, in order to assess the practicality of the proposed methodology in real-world scenarios, we utilized the brain-computer interface (BCI) competition III-dataset II. Main results. Our primary results demonstrate the superior performance of our approach compared to conventional methods commonly employed for single-trial estimation. Additionally, our method outperformed both Tucker decomposition and non-negative Tucker decomposition in the synthesized dataset. Furthermore, the results obtained from real-world data exhibited meaningful performance and provided insightful interpretations for the extracted P300 component. Significance. The findings suggest that the proposed decomposition is eminently capable of extracting the target P300 component's waveform, including latency and amplitude as well as its spatial location, using single-trial EEG recordings., (Creative Commons Attribution license.)

Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task. In rapid serial visual presentation (RSVP) tasks, the subject is presented with a continuous stream of images containing rare target images among standard images, while the algorithm has to detect brain activity associated with target images. In this work, we suggest a multimodal neural network for RSVP tasks. The network operates on the brain response and on the initiating stimulus simultaneously, providing more information for the BCI application. We present two variants of the multimodal network, a supervised model, for the case when the targets are known in advanced, and a semi-supervised model for when the targets are unknown. We test the neural networks with a RSVP experiment on satellite imagery carried out with two subjects. The multimodal networks achieve a significant performance improvement in classification metrics. We visualize what the networks has learned and discuss the advantages of using neural network models for BCI applications. [ABSTRACT FROM AUTHOR]

Background Progress in neuroscience depends substantially on the ability to establish the detailed spatial and temporal sequence of neuronal population-level activity across large areas of the brain. Because there is substantial inter-trial variability in neuronal activity, traditional techniques that rely on signal averaging obscure where and when neuronal activity occurs. Thus, up to the present, it has been difficult to examine the detailed progression of neuronal activity across large areas of the brain. New method Here we describe a method for establishing the spatiotemporal evolution of neuronal population-level activity across large brain regions by determining exactly where and when neural activity occurs during a behavioral task in individual trials. We validate the efficacy of the method, examine the effects of its parameterization, and demonstrate its utility by highlighting two sets of results that could not readily be achieved with traditional methods. Results Our results reveal the precise spatiotemporal evolution of neuronal population activity that unfolds during a sensorimotor task in individual trials, and establishes the relationship between neuronal oscillations and the onset of this activity. Conclusions The ability to identify the spatiotemporal evolution of neuronal population activity onsets in single trials gives investigators a powerful new tool with which to study large-scale cortical processes. [ABSTRACT FROM AUTHOR]

A single-trial extraction of a visual evoked potential (VEP) signal based on the partial least-squares (PLS) regression method has been proposed in this paper. This paper has focused on the extraction and estimation of the latencies of P100, P200, P300, N75, and N135 in the artificial electroencephalograph (EEG) signal. The real EEG signal obtained from the hospital was only concentrated on the P100. The performance of the PLS has been evaluated mainly on the basis of latency error rate of the peaks for the artificial EEG signal, and the mean peak detection and standard deviation for the real EEG signal. The simulation results show that the proposed PLS algorithm is capable of reconstructing the EEG signal into its desired shape of the ideal VEP. For P100, the proposed PLS algorithm is able to provide comparable results to the generalized eigenvalue decomposition (GEVD) algorithm, which alters (prewhitens) the EEG input signal using the prestimulation EEG signal. It has also shown better performance for later peaks (P200 and P300). The PLS outperformed not only in positive peaks but also in N75. In P100, the PLS was comparable with the GEVD although N135 was better estimated by GEVD. The proposed PLS algorithm is comparable to GEVD given that PLS does not alter the EEG input signal. The PLS algorithm gives the best estimate to multitrial ensemble averaging. This research offers benefits such as avoiding patient's fatigue during VEP test measurement in the hospital, in BCI applications and in EEG-fMRI integration. [ABSTRACT FROM PUBLISHER]

A major effort in cognitive neuroscience of language is to define the temporal and spatial characteristics of the core cognitive processes involved in word production. One approach consists in studying the effects of linguistic and pre-linguistic variables in picture naming tasks. So far, studies have analyzed event-related potentials (ERPs) during word production by examining one or two variables with factorial designs. Here we extended this approach by investigating simultaneously the effects of multiple theoretical relevant predictors in a picture naming task. High density EEG was recorded on 31 participants during overt naming of 100 pictures. ERPs were extracted on a trial by trial basis from picture onset to 100 msec before the onset of articulation. Mixed-effects regression models were conducted to examine which variables affected production latencies and the duration of periods of stable electrophysiological patterns (topographic maps). Results revealed an effect of a pre-linguistic variable, visual complexity, on an early period of stable electric field at scalp, from 140 to 180 after picture presentation, a result consistent with the proposal that this time period is associated with visual object recognition processes. Three other variables, word age of acquisition, name agreement and image agreement, influenced response latencies and modulated ERPs from ~380 msec to the end of the analyzed period. These results demonstrate that a topographic analysis fitted into the single trial ERPs and covering the entire processing period allows one to associate the cost generated by psycholinguistic variables to the duration of specific stable electrophysiological processes and to pinpoint the precise time-course of multiple word production predictors at once.

For day-to-day decisions, multiple factors influence our choice between alternatives. Two dimensions of decision making that substantially affect choice are the objective perceptual properties of the stimulus (e.g., salience) and its subjective value. Here we measure EEGs in human subjects to relate their feedback-evoked EEG responses to estimates of prediction error given a neurally derived expected value for each trial. Unlike in traditional reinforcement learning paradigms, in our experiment the reward itself is not probabilistic; rather, it is a fixed value, which, when combined with the variable stimulus salience, yields uncertainty in the choice. We find that feedback-evoked event-related potentials (ERPs), specifically those classically termed feedback-related negativity, are modulated by both the reward level and stimulus salience. Using single-trial analysis of the EEG, we show stimulus-locked EEG components reflecting perceived stimulus salience can be combined with the level of reward to create an estimate of expected reward. This expected reward is used to form a prediction error that correlates with the trial-by-trial variability of the feedback ERPs for negative, but not positive, feedback. This suggests that the valence of prediction error is more important than the valence of the actual feedback, since only positive rewards were delivered in the experiment (no penalty or loss). Finally, we show that these subjectively defined prediction errors are informative of the riskiness of the subject's choice on the subsequent trial. In summary, our work shows that neural correlates of stimulus salience interact with value information to yield neural representations of subjective expected reward. [ABSTRACT FROM AUTHOR]

Application of neuro-augmentation technology based on dry-wireless EEG may be considerably beneficial for aviation and space operations because of the inherent dangers involved. In this study we evaluate classification performance of perceptual events using a dry-wireless EEG system during motion platform based flight simulation and actual flight in an open cockpit biplane to determine if the system can be used in the presence of considerable environmental and physiological artifacts. A passive task involving 200 random auditory presentations of a chirp sound was used for evaluation. The advantage of this auditory task is that it does not interfere with the perceptual motor processes involved with piloting the plane. Classification was based on identifying the presentation of a chirp sound vs. silent periods. Evaluation of Independent component analysis (ICA) and Kalman filtering to enhance classification performance by extracting brain activity related to the auditory event from other non-task related brain activity and artifacts was assessed. The results of permutation testing revealed that single trial classification of presence or absence of an auditory event was significantly above chance for all conditions on a novel test set. The best performance could be achieved with both ICA and Kalman filtering relative to no processing: Platform Off (83.4% vs. 78.3%), Platform On (73.1% vs. 71.6%), Biplane Engine Off (81.1% vs. 77.4%), and Biplane Engine On (79.2% vs. 66.1%). This experiment demonstrates that dry-wireless EEG can be used in environments with considerable vibration, wind, acoustic noise, and physiological artifacts and achieve good single trial classification performance that is necessary for future successful application of neuro-augmentation technology based on brain-machine interfaces. [ABSTRACT FROM AUTHOR]

A major effort in cognitive neuroscience of language is to define the temporal and spatial characteristics of the core cognitive processes involved in word production. One approach consists in studying the effects of linguistic and pre-linguistic variables in picture naming tasks. So far, studies have analyzed event-related potentials (ERPs) during word production by examining one or two variables with factorial designs. Here we extended this approach by investigating simultaneously the effects of multiple theoretical relevant predictors in a picture naming task. High density EEG was recorded on 31 participants during overt naming of 100 pictures. ERPs were extracted on a trial by trial basis from picture onset to 100ms before the onset of articulation. Mixed-effects regression models were conducted to examine which variables affected production latencies and the duration of periods of stable electrophysiological patterns (topographic maps). Results revealed an effect of a pre-linguistic variable, visual complexity, on an early period of stable electric field at scalp, from 140 to 180ms after picture presentation, a result consistent with the proposal that this time period is associated with visual object recognition processes. Three other variables, word Age of Acquisition, Name Agreement, and Image Agreement influenced response latencies and modulated ERPs from ~380ms to the end of the analyzed period. These results demonstrate that a topographic analysis fitted into the single trial ERPs and covering the entire processing period allows one to associate the cost generated by psycholinguistic variables to the duration of specific stable electrophysiological processes and to pinpoint the precise time-course of multiple word production predictors at once. [ABSTRACT FROM AUTHOR]

Ultimately associative learning is a function of the temporal features and relationships between experienced stimuli. Nevertheless how time affects the neural circuit underlying this form of learning remains largely unknown. To address this issue, we used single-trial auditory trace fear conditioning and varied the length of the interval between tone and foot-shock. Through temporary inactivation of the amygdala, medial-prefrontal cortex (mPFC) and dorsal-hippocampus in rats, we tested the hypothesis that different temporal intervals between the tone and the shock influence the neuronal structures necessary for learning. Here, we show for the first time that the amygdala is critically involved in the acquisition of auditory fear learning when there is a temporal gap between the tone and the shock. Moreover, imposing a short interval (5 s) between the two stimuli also recruits the medial pre-frontal cortex (mPFC), while learning the association across a longer interval (40 s) becomes additionally dependent on a third structure, the dorsal-hippocampus. Thus, our results show that increasing the interval length between tone and shock leads to the requirement of an increasing number of brain areas for the association between the two stimuli to be acquired. These findings demonstrate that the temporal relationship between events is a key factor in determining the neuronal mechanisms underlying associative fear learning.

Using a rapid serial visual presentation paradigm, we previously showed that the average amplitudes of six event-related potential (ERP) components were affected by different categories of emotional faces. In the current study, we investigated the six discriminating components on a single-trial level to clarify whether the amplitude difference between experimental conditions results from a difference in the real variability of single-trial amplitudes or from latency jitter across trials. It is found that there were consistent amplitude differences in the single-trial P1, N170, VPP, N3, and P3 components, demonstrating that a substantial proportion of the average amplitude differences can be explained by the pure variability in amplitudes on a single-trial basis between experimental conditions. These single-trial results verified the three-stage scheme of facial expression processing beyond multitrial ERP averaging, and showed the three processing stages of 'fear popup', 'emotional/unemotional discrimination', and 'complete separation' based on the single-trial ERP dynamics. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Theta-alpha range oscillations have been associated with MMN in healthy controls. Our previous studies showed that theta-alpha activities are highly heritable in schizophrenia patients’ families. We aimed to test the hypothesis that theta-alpha activities may contribute to MMN in schizophrenia patients and their family members. Methods: We compared MMN and single trial oscillations during MMN in 95 patients, 75 first-degree relatives, 87 controls, and 34 community subjects with schizophrenia spectrum personality (SSP) traits. Results: We found that (1) MMN was reduced in patients (p <0.001) and SSP subjects (p =0.047) but not in relatives (p =0.42); (2) there were augmented 1–20Hz oscillations in patients (p =0.02 to <0.001) during standard and deviant stimuli; (3) theta-alpha (5–12Hz) oscillations had the strongest correlation to MMN in controls and relatives (ΔR 2 =21.4–23.9%, all p <0.001), while delta (<5Hz) showed the strongest correlation to MMN in schizophrenia and SSP trait subjects; and, (4) MMN (h 2 =0.56, p =0.002) and theta-alpha (h 2 =0.55, p =0.004) were heritable traits. Conclusions: Low frequency oscillations have a robust relationship with MMN and the relationship appears altered by schizophrenia; and schizophrenia patients showed augmented low frequency activities during the MMN paradigm. Significance: The results encourage investigation of low frequency oscillations to elucidate the neurophysiological pathology underlying MMN abnormalities in schizophrenia. [Copyright &y& Elsevier]

Abstract: In the early days of fMRI, data were acquired using methods adapted mainly from PET imaging. Sets of similar stimuli were presented in extended blocks, with stimulus conditions changing from block to block. While this method provided optimum statistical power, it also presented a variety of potential confounds, including changes in attention, alertness, expectancy, and practice effects within and between blocks. Event-related paradigms using unpredictable, randomized stimulus sequences had been used in EEG studies for over 50years before the development of fMRI, and provided a means to overcome these issues. However, the temporal dispersion of BOLD fMRI activity resulted in responses to successive stimuli adding together, making it difficult to perform rapid event-related paradigms using fMRI. Here we describe the background and history of methods developed to overcome this limitation, allowing rapid, randomized stimulus sequences to be used with fMRI. The advantages and disadvantages of this technique and how these methods have been applied in a variety of experimental settings are discussed. [Copyright &y& Elsevier]

Abstract: Objective: We evaluated the potential of recurrence quantification analysis (RQA) to improve the analysis of trial-by-trial-variability in event-related potentials (ERPs) experiments. Methods: We use an acoustic oddball paradigm to compare the efficiency of RQA with a linear amplitude based analysis of single trial ERPs with regard to the power to distinguish responses to different tone types. We further probed the robustness of both analyses towards structured noise induced by parallel magnetic resonance imaging (MRI). Results: RQA provided robust discrimination of responses to different tone types, even when EEG data were contaminated by structured noise. Yet, its power to discriminate responses to different tone types was not significantly superior to a linear amplitude analysis. RQA measures were only moderately correlated with EEG amplitudes, suggesting that RQA may extract additional information from single trial responses not detected by amplitude evaluation. Conclusions: RQA allows quantifying signal characteristics of single trial ERPs measured with and without noise induced by parallel MRI. RQA power to discriminate responses to different tone types was similar to linear amplitude based analysis. Significance: RQA has the potential to detect differences of signal features in response to a standard oddball paradigm and provide additional trial-by-trial information compared to classical amplitude based analysis. [Copyright &y& Elsevier]

Searching for target images in large volume imagery is a challenging problem and the rapid serial visual presentation (RSVP) triage is potentially a promising solution to the problem. RSVP triage is essentially a cortically-coupled computer vision technique that relies on single-trial detection of event-related potentials (ERP). In RSVP triage, images are shown to a subject in a rapid serial sequence. When a target image is seen by the subject, unique ERP characterized by P300 are elicited. Thus, in RSVP triage, accurate detection of such distinct ERP allows for fast searching of target images in large volume imagery. The accuracy of the distinct ERP detection in RSVP triage depends on the feature extraction method, for which the common spatial pattern analysis (CSP) was used with limited success. This paper presents a novel feature extraction method, termed common spatio-temporal pattern (CSTP), which is critical for robust single-trial detection of ERP. Unlike the conventional CSP, whereby only spatial patterns of ERP are considered, the present proposed method exploits spatial and temporal patterns of ERP separately, providing complementary spatial and temporal features for high accurate single-trial ERP detection. Numerical study using data collected from 20 subjects in RSVP triage experiments demonstrates that the proposed method offers significant performance improvement over the conventional CSP method (corrected p-value < 0.05, Pearson r=0.64) and other competing methods in the literature. This paper further shows that the main idea of CSTP can be easily applied to other methods. [ABSTRACT FROM PUBLISHER]

Abstract: The readiness potential (RP), a slow negative electroencephalographic pre-movement potential, was reported to commence earlier for movements with the non-dominant left hand than with the dominant right hand. Latencies in these reports were always calculated from averaged RPs, whereas onset times of individual trials remained inaccessible. The aim was to use a new statistical approach to examine whether a few left hand trials with very early pre-movement activity disproportionally affect the onset of the average. We recorded RPs in 28 right-handed subjects while they made self-paced repetitive unilateral movements with their dominant and non-dominant hand. Skewness, a measure of distribution asymmetry, was analysed in sets of single-trial RPs to discriminate between a symmetric distribution and an asymmetric distribution containing outlier trials with early onset. Results show that for right hand movements skewness has values around zero across electrodes and pre-movement intervals, whereas for left hand movements skewness has initially negative values which increase to neutral values closer to movement onset. This indicates a symmetric (e.g., Gaussian) distribution of onset times across trials for simple right hand movements, whereas cortical activation preceding movements with the non-dominant hand is characterised by outlier trials with early onset of negativity. These findings may explain differences in the averaged brain activation preceding dominant versus non-dominant hand movements described in previous electrophysiological/neuroimaging studies. The findings also constrain mental chronometry, a technique that makes conclusions upon the time and temporal order of brain processes by measuring and comparing onset times of averaged electroencephalographic potentials evoked by these processes. [Copyright &y& Elsevier]

Abstract: Behavioral and electrophysiological data related to performance in an auditory Go/NoGo task were analyzed in young and older adults in the present study. Especially, differences in within-person variability in behavior and neural activity between young and older adults and changes in topography of slow event-related oscillations (EROs) were of interest. Within-person variability in behavior was assessed by reaction time (RT) variability. Event-related delta and theta oscillations were analyzed using time–frequency transformation, which can give information on the time-course of single trial event-related EEG spectral power enhancement and intertrial phase-locking (ITC). In contrast to our previous visual Go/NoGo study, no under-recruitment of task-relevant brain regions was found for the auditory theta and delta EROs. Young did not differ from older adults in RT variability or in single trial delta/theta ITC. Altered recruitment of brain activity at advanced age was indicated, first, by stronger early theta phase-locking in older compared to young adults and, second, by a Go-specific lateralization of delta/theta activity. We conclude that within-person variability may increase with age, but the degree depends on performance level and the modality investigated. [Copyright &y& Elsevier]

One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis. [ABSTRACT FROM AUTHOR]

Abstract: The classical approach for extracting event-related potentials (ERPs) from the brain is ensemble averaging. For long latency ERPs this is not optimal, partly due to the time-delay in obtaining a response and partly because the latency and amplitude for the ERP components, like the P300, are variable and depend on cognitive function. This study compares the performance of a simplified Kalman filter with Sliding Window Averaging in tracking dynamical changes in single trial P300. The comparison is performed on simulated P300 data with added background noise consisting of both simulated and real background EEG in various input signal to noise ratios. While both methods can be applied to track dynamical changes, the simplified Kalman filter has an advantage over the Sliding Window Averaging, most notable in a better noise suppression when both are optimized for faster changing latency and amplitude in the P300 component and in a considerably higher robustness towards suboptimal settings. The latter is of great importance in a clinical setting where the optimal setting cannot be determined. [ABSTRACT FROM AUTHOR]