Single-trial estimation of multichannel evoked-potential measurements

Single-trial event-related potential extraction through one-unit ICA-with-reference

OBJECTIVE

In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed.

APPROACH

In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly.

MAIN RESULTS

Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction.

SIGNIFICANCE

In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

A multi-task learning approach for the extraction of single-trial evoked potentials

Evoked potentials (EPs) are of great interest in neuroscience, but their measurement is difficult as they are embedded in background spontaneous electroencephalographic (EEG) activity which has a much larger amplitude. The widely used averaging technique requires the delivery of a large number of identical stimuli and yields only an "average" EP which does not allow the investigation of the possible variability of single-trial EPs. In the present paper, we propose the use of a multi-task learning method (MTL) for the simultaneous extraction of both the average and the N single-trial EPs from N recorded sweeps. The technique is developed within a Bayesian estimation framework and uses flexible stochastic models to describe the average response and the N shifts between the single-trial EPs and this average. Differently from other single-trial estimation approaches proposed in the literature, MTL can provide estimates of both the average and the N single-trial EPs in a single stage. In the present paper, MTL is successfully assessed on both synthetic (100 simulated recording sessions with N=20 sweeps) and real data (11 subjects with N=20 sweeps) relative to a cognitive task carried out for the investigation of the P300 component of the EP.

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.

Single-trial evoked potentials study by combining wavelet denoising and principal component analysis methods

The authors have developed a new approach by combining the wavelet denoising and principal component analysis methods to reduce the number of required trials for efficient extraction of brain evoked-related potentials (ERPs). Evoked-related potentials were initially extracted using wavelet denoising to enhance the signal-to-noise ratio of raw EEG measurements. Principal components of ERPs accounting for 80% of the total variance were extracted as part of the subspace of the ERPs. Finally, the ERPs were reconstructed from the selected principal components. Computer simulation results showed that the combined approach provided estimations with higher signal-to-noise ratio and lower root mean squared error than each of them alone. The authors further tested this proposed approach in single-trial ERPs extraction during an emotional process and brain responses analysis to emotional stimuli. The experimental results also demonstrated the effectiveness of this combined approach in ERPs extraction and further supported the view that emotional stimuli are processed more intensely.

Graph-based variability estimation in single-trial event-related neural responses

Extracting information from multitrial magnetoencephalography or electroencephalography (EEG) recordings is challenging because of the very low SNR, and because of the inherent variability of brain responses. The problem of low SNR is commonly tackled by averaging multiple repetitions of the recordings, also called trials, but the variability of response across trials leads to biased results and limits interpretability. This paper proposes to decode the variability of neural responses by making use of graph representations. Our approach has several advantages compared to other existing methods that process single-trial data: first, it avoids the a priori definition of a model for the waveform of the neural response; second, it does not make use of the average data for parameter estimation; third, it does not suffer from initialization problems by providing solutions that are global optimum of cost functions; and last, it is fast. We proceed in two steps. First, a manifold learning algorithm, based on a graph Laplacian, offers an efficient way of ordering trials with respect to the response variability, under the condition that this variability itself depends on a single parameter. Second, the estimation of the variability is formulated as a combinatorial optimization that can be solved very efficiently using graph cuts. Details and validation of this second step are provided for latency estimation. Performance and robustness experiments are conducted on synthetic data, and results are presented on EEG data from a P300 oddball experiment.

On correlation between single-trial ERP and GSR responses: a principal component regression approach

In this study we investigate the correlation between single-trial evoked brain responses and galvanic skin responses (GSR). The correlation between the two signals is examined by using a modified principal component regression based approach. A potential application of the study is to utilize the GSR measurements in a form of a prior information in the estimation of the brain potentials when only small number of trials is available.

Estimation of individual evoked potential components using iterative independent component analysis

Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.

Development and evaluation of the sparse decomposition method with mixed over-complete dictionary for evoked potential estimation

A new method is developed to decompose a physiological signal into a summation of transient and oscillatory components, referred to as mixed over-complete dictionary based sparse component decomposition algorithm (MOSCA). Based on the characteristics of the transient evoked potential (EP) and the background noise, the mixed dictionary is constructed with an over-complete wavelet dictionary and an over-complete discrete cosine (DC) function dictionary, and the signal is separated by learning in this mixed dictionary with a matching pursuit (MP) algorithm. MOSCA is designed specifically for the separation of a desired transient EP from the existing spontaneous EEG or other background noise. The method was evaluated with several simulation tests in which EPs or simulated EPs were deeply masked in different strong noise backgrounds, and the recovered signal is similar to the original assumed EP with a high and stable correlation coefficient (CC). The method was then applied to estimate event related potential (ERP) in the classical oddball experiment, and the results confirmed that the trial number for a reliable ERP estimation might be greatly reduced by MOSCA.

Adaptive Time-Frequency Models for Single-Trial M/EEG Analysis

A new method is introduced for estimating single-trial magneto- or electro-encephalography (M/EEG), based on a non-linear fit of time-frequency atoms. The method can be applied for transient activity (e.g. event-related potentials) as well as for oscillatory activity (e.g. gamma bursts), and for both evoked or induced activity. In order to benefit from all the structure present in the data, the method accounts for (i) spatial structure of the data via multivariate decomposition, (ii) time-frequency structure via atomic decomposition and (iii) reproducibility across trials via a constraint on parameter dispersion. Moreover, a novel iterative method is introduced for estimating the initial time-frequency atoms used in the non-linear fit. Numerical experiments show that the method is robust to low signal-to-noise conditions, and that the introduction of the constraint on parameter dispersion significantly improves the quality of the fit.

Single-Trial Dynamical Estimation of Event-Related Potentials: A Kalman Filter-Based Approach

A method for single-trial dynamical estimation of event-related potentials (ERPs) is presented. The method is based on recursive Bayesian mean square estimation and the estimators are obtained with a Kalman filtering procedure. We especially focus on the case that previous trials contain prior information of relevance to the trial being analyzed. The potentials are estimated sequentially using the previous estimates as prior information. The performance of the method is evaluated with simulations and with real P300 responses measured using auditory stimuli. Our approach is shown to have excellent capability of estimating dynamic changes form stimulus to stimulus present in the parameters of the ERPs, even in poor signal-to-noise ratio (SNR) conditions.

Systematic latency variation of the auditory evoked M100: from average to single-trial data

Standard analyses of neurophysiologically evoked response data rely on signal averaging across many epochs associated with specific events. The amplitudes and latencies of these averaged events are subsequently interpreted in the context of the given perceptual, motor, or cognitive tasks. Can such critical timing properties of event-related responses be recovered from single-trial data? Here, we make use of the M100 latency paradigm used in previous magnetoencephalography (MEG) research to evaluate a novel single-trial analysis approach. Specifically, the latency of the auditory evoked M100 varies systematically with stimulus frequency over a well-defined time range (lower frequencies, e.g., 125 Hz, yield up to 25 ms longer latencies than higher frequencies, e.g., 1000 Hz). Here, we show that the complex filtering approach to single-trial analysis recovers this key characteristic of the M100 response, as well as some other important response properties relating to lateralization. The results illustrate (i) the utility of the complex filtering method and (ii) the potential of the M100 latency to be used for stimulus encoding, since the relevant variation can be observed in single trials.