Single-trial P300 estimation with a spatiotemporal filtering method

ETucker: a constrained tensor decomposition for single trial ERP extraction

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.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

Enhancing P300-BCI performance using latency estimation

Brain Computer Interfaces (BCIs) offer restoration of communication to those with the most severe movement impairments, but performance is not yet ideal. Previous work has demonstrated that latency jitter, the variation in timing of the brain responses, plays a critical role in determining BCI performance. In this study, we used Classifier-Based Latency Estimation (CBLE) and a wavelet transform to provide information about latency jitter to a second-level classifier. Three second-level classifiers were tested: least squares (LS), step-wise linear discriminant analysis (SWLDA), and support vector machine (SVM). Of these three, LS and SWLDA performed better than the original online classifier. The resulting combination demonstrated improved detection of brain responses for many participants, resulting in better BCI performance. Interestingly, the performance gain was greatest for those individuals for whom the BCI did not work well online, indicating that this method may be most suitable for improving performance of otherwise marginal participants.

The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis

We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.

The anatomical and functional relationship between the P3 and autonomic components of the orienting response

Many psychophysiologists have noted the striking similarities between the antecedent conditions for the P3 component of the event-related potential and the orienting response: both are typically elicited by salient, unexpected, novel, task-relevant, and other motivationally significant stimuli. Although the close coupling of the P3 and orienting response has been well documented, the neural basis and functional role of this relationship is still poorly understood. Here we propose that the simultaneous occurrence of the P3 and autonomic components of the orienting response reflects the co-activation of the locus coeruleus-norepinephrine system and the peripheral sympathetic nervous system by their common major afferent: the rostral ventrolateral medulla, a key sympathoexcitatory region. A comparison of the functional significance of the locus coeruleus-norepinephrine system and the peripheral sympathetic nervous system suggests that the P3 and orienting response reflect complementary cognitive and physical contributions to the mobilization for action following motivationally significant stimuli.

Beta receptor-mediated modulation of the oddball P3 but not error-related ERP components in humans

RATIONALE

The P3 is a ubiquitous component of stimulus-driven neural activity that can be observed in scalp electrophysiological recordings. Multiple lines of evidence suggest an important role for the noradrenergic system in the generation of the P3. However, pharmacological studies of the P3 using noradrenergic manipulations have so far been limited to agents that affect α2-receptor signaling.

OBJECTIVES

The present study investigated whether β-adrenergic receptors are involved in the generation of the P3 and the error positivity (Pe), a component of the event-related potential that is elicited by errors and that bears many similarities to the P3.

METHODS

We used a double-blind, placebo-controlled, crossover design in which we examined in human participants (N = 16) the effect of a single dose of propranolol (80 mg) on the amplitudes of the P3 observed in visual and auditory oddball tasks and the Pe observed in a flanker task.

RESULTS

We found that P3s to auditory stimuli were increased in amplitude following treatment with propranolol. Propranolol also modulated the P3 to visual stimuli, but in a direction dependent on participants' level of trait anxiety: In participants with lower trait anxiety, propranolol resulted in a (non-significant) decrease in P3 amplitudes; in participants with higher trait anxiety, propranolol significantly enhanced P3 amplitude. Propranolol did not modulate the amplitude of the Pe or behavioral measures of conflict/error-related performance adjustments.

CONCLUSIONS

These results provide the first evidence for involvement of β-adrenergic receptors in P3 generation. We speculate that propranolol affected the P3 through actions at β2-receptors in the locus coeruleus.

Insight into the relationship between brain/behavioral speed and variability in patients with minimal hepatic encephalopathy

OBJECTIVE

Intra-individual variability (IIV) of response reaction times (RTs) and psychomotor slowing were proposed as markers of brain dysfunction in patients with minimal hepatic encephalopathy (MHE), a subclinical disorder of the central nervous system frequently detectable in patients with liver cirrhosis. However, behavioral measures alone do not enable investigations into the neural correlates of these phenomena. The aim of this study was to investigate the electrophysiological correlates of psychomotor slowing and increased IIV of RTs in patients with MHE.

METHODS

Event-related potentials (ERPs), evoked by a stimulus-response (S-R) conflict task, were recorded from a sample of patients with liver cirrhosis, with and without MHE, and a group of healthy controls. A recently presented Bayesian approach was used to estimate single-trial P300 parameters.

RESULTS

Patients with MHE, with both psychomotor slowing and higher IIV of RTs, showed higher P300 latency jittering and lower single-trial P300 amplitude compared to healthy controls. In healthy controls, distribution analysis revealed that single-trial P300 latency increased and amplitude decreased as RTs became longer; however, in patients with MHE the linkage between P300 and RTs was weaker or even absent.

CONCLUSIONS

These findings suggest that in patients with MHE, the loss of the relationship between P300 parameters and RTs is related to both higher IIV of RTs and psychomotor slowing.

SIGNIFICANCE

This study highlights the utility of investigating the relationship between single-trial ERPs parameters along with RT distributions to explore brain functioning in normal or pathological conditions.

Multivariate temporal dictionary learning for EEG

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.

Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy

OBJECTIVE

Brain-computer interfaces (BCIs) that detect event-related potentials (ERPs) rely on classification schemes that are vulnerable to latency jitter, a phenomenon known to occur with ERPs such as the P300 response. The objective of this work was to investigate the role that latency jitter plays in BCI classification.

APPROACH

We developed a novel method, classifier-based latency estimation (CBLE), based on a generalization of Woody filtering. The technique works by presenting the time-shifted data to the classifier, and using the time shift that corresponds to the maximal classifier score.

MAIN RESULTS

The variance of CBLE estimates correlates significantly (p < 10(-42)) with BCI accuracy in the Farwell-Donchin BCI paradigm. Additionally, CBLE predicts same-day accuracy, even from small datasets or datasets that have already been used for classifier training, better than the accuracy on the small dataset (p < 0.05). The technique should be relatively classifier-independent, and the results were confirmed on two linear classifiers.

SIGNIFICANCE

The results suggest that latency jitter may be an important cause of poor BCI performance, and methods that correct for latency jitter may improve that performance. CBLE can also be used to decrease the amount of data needed for accuracy estimation, allowing research on effects with shorter timescales.

Acute tryptophan depletion attenuates brain-heart coupling following external feedback

External and internal performance feedback triggers neural and visceral modulations such as reactions in the medial prefrontal cortex and insulae or changes of heart period (HP). The functional coupling of neural and cardiac responses following feedback (cortico-cardiac connectivity) is not well understood. While linear time-lagged within-subjects correlations of single-trial EEG and HP (cardio-electroencephalographic covariance tracing, CECT) indicate a robust negative coupling of EEG magnitude 300 ms after presentation of an external feedback stimulus with subsequent alterations of heart period (the so-called N300H phenomenon), the neurotransmitter systems underlying feedback-evoked cortico-cardiac connectivity are largely unknown. Because it has been shown that acute tryptophan depletion (ATD), attenuating brain serotonin (5-HT), decreases cardiac but not neural correlates of feedback processing, we hypothesized that 5-HT may be involved in feedback-evoked cortico-cardiac connectivity. In a placebo-controlled double-blind cross-over design, 12 healthy male participants received a tryptophan-free amino-acid drink at one session (TRP−) and a balanced amino-acid control-drink (TRP+) on another and twice performed a time-estimation task with feedback presented after each trial. N300H magnitude and plasma tryptophan levels were assessed. Results indicated a robust N300H after TRP+, which was significantly attenuated following TRP−. Moreover, plasma tryptophan levels during TRP+ were correlated with N300H amplitude such that individuals with lower tryptophan levels showed reduced N300H. Together, these findings indicate that 5-HT is important for feedback-induced covariation of cortical and cardiac activity. Because individual differences in anxiety have previously been linked to 5-HT, cortico-cardiac coupling and feedback processing, the present findings may be particularly relevant for futures studies on the relationship between 5-HT and anxiety.

A Bayesian method to estimate single-trial event-related potentials with application to the study of the P300 variability

We propose a Bayesian method to extract single-trial event related potentials (ERPs). The method is formulated in two stages. In the first stage, each of the N raw sweeps is processed by an individual "optimal" filter, where the 2nd order a priori statistical information on the background EEG and on the unknown ERP is, respectively, estimated from pre-stimulus data and obtained through the multiple integration of a white noise process model which is identifiable from post-stimulus data thanks to a smoothing criterion. Then, a mean ERP is determined as the weighted average of the filtered sweeps, where each weight is inversely proportional to the expected value of the norm of the correspondent filter error. In the second stage, single-sweep estimation is dealt with within the same framework, by using the average ERP estimated in the previous stage as a priori expected response. The method is successfully tested on simulated data and then employed on real data with the aim of investigating the variability of the P300 component during a cognitive visual task. A comparison with other literature methods is also performed. Results encourage further use of the proposed method to investigate if and how diseases, e.g., cirrhosis, are associated to differences in P300 variability.