Dataset Information

A novel single-trial event-related potential estimation method based on compressed sensing.

ABSTRACT: Cognitive functions are often studied using event-related potentials (ERPs) that are usually estimated by an averaging algorithm. Clearly, estimation of single-trial ERPs can provide researchers with many more details of cognitive activity than the averaging algorithm. A novel method to estimate single-trial ERPs is proposed in this paper. This method includes two key ideas. First, singular value decomposition was used to construct a matrix, which mapped single-trial electroencephalographic recordings (EEG) into a low-dimensional vector that contained little information from the spontaneous EEG. Second, we used the theory of compressed sensing to build a procedure to restore single-trial ERPs from this low-dimensional vector. ERPs are sparse or approximately sparse in the frequency domain. This fact allowed us to use the theory of compressed sensing. We verified this method in simulated and real data. Our method and dVCA (differentially variable component analysis), another method of single-trial ERPs estimation, were both used to estimate single-trial ERPs from the same simulated data. Results demonstrated that our method significantly outperforms dVCA under various conditions of signal-to-noise ratio. Moreover, the single-trial ERPs estimated from the real data by our method are statistically consistent with the theories of cognitive science.

SUBMITTER: Huang Z

PROVIDER: S-EPMC5562551 | biostudies-other | 2013 Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:The event-related potential (ERP) is the brain response measured in electroencephalography (EEG), which reflects the process of human cognitive activity. ERP has been introduced into brain computer interfaces (BCIs) to communicate the computer with the subject's intention. Due to the low signal-to-noise ratio of EEG, most ERP studies are based on grand-averaging over many trials. Recently single-trial ERP detection attracts more attention, which enables real time processing tasks as rapid face identification. All the targets needed to be retrieved may appear only once, and there is no knowledge of target label for averaging. More interestingly, how the features contribute temporally and spatially to single-trial ERP detection has not been fully investigated. In this paper, we propose to implement a local-learning-based (LLB) feature extraction method to investigate the importance of spatial-temporal components of ERP in a task of rapid face identification using single-trial detection. Comparing to previous methods, LLB method preserves the nonlinear structure of EEG signal distribution, and analyze the importance of original spatial-temporal components via optimization in feature space. As a data-driven methods, the weighting of the spatial-temporal component does not depend on the ERP detection method. The importance weights are optimized by making the targets more different from non-targets in feature space, and regularization penalty is introduced in optimization for sparse weights. This spatial-temporal feature extraction method is evaluated on the EEG data of 15 participants in performing a face identification task using rapid serial visual presentation paradigm. Comparing with other methods, the proposed spatial-temporal analysis method uses sparser (only 10% of the total) features, and could achieve comparable performance (98%) of single-trial ERP detection as the whole features across different detection methods. The interesting finding is that the N250 is the earliest temporal component that contributes to single-trial ERP detection in face identification. And the importance of N250 components is more laterally distributed toward the left hemisphere. We show that using only the left N250 component over-performs the right N250 in the face identification task using single-trial ERP detection. The finding is also important in building a fast and efficient (fewer electrodes) BCI system for rapid face identification.

Project description:Cardiac magnetic resonance (CMR) is a key tool for cardiac work-up. However, arrhythmia can be responsible for arrhythmia-related artifacts (ARA) and increased scan time using segmented sequences. The aim of this study is to evaluate the effect of cardiac arrhythmia on image quality in a comparison of a compressed sensing real-time (CSrt) cine sequence with the reference prospectively gated segmented balanced steady-state free precession (Cineref) technique regarding ARA. A total of 71 consecutive adult patients (41 males; mean age = 59.5 ± 20.1 years (95% CI: 54.7-64.2 years)) referred for CMR examination with concomitant irregular heart rate (defined by an RR interval coefficient of variation >10%) during scanning were prospectively enrolled. For each patient, two cine sequences were systematically acquired: first, the reference prospectively triggered multi-breath-hold Cineref sequence including a short-axis stack, one four-chamber slice, and a couple of two-chamber slices; second, an additional single breath-hold CSrt sequence providing the same slices as the reference technique. Two radiologists independently assessed ARA and image quality (overall, acquisition, and edge sharpness) for both techniques. The mean heart rate was 71.8 ± 19.0 (SD) beat per minute (bpm) (95% CI: 67.4-76.3 bpm) and its coefficient of variation was 25.0 ± 9.4 (SD) % (95% CI: 22.8-27.2%). Acquisition was significantly faster with CSrt than with Cineref (Cineref: 556.7 ± 145.4 (SD) s (95% CI: 496.7-616.7 s); CSrt: 23.9 ± 7.9 (SD) s (95% CI: 20.6-27.1 s); p < 0.0001). A total of 599 pairs of cine slices were evaluated (median: 8 (range: 6-14) slices per patient). The mean proportion of ARA-impaired slices per patient was 85.9 ± 22.7 (SD) % using Cineref, but this was figure was zero using CSrt (p < 0.0001). The European CMR registry artifact score was lower with CSrt (median: 1 (range: 0-5)) than with Cineref (median: 3 (range: 0-3); p < 0.0001). Subjective image quality was higher in CSrt than in Cineref (median: 3 (range: 1-3) versus 2 (range: 1-4), respectively; p < 0.0001). In line, edge sharpness was higher on CSrt cine than on Cineref images (0.054 ± 0.016 pixel-1 (95% CI: 0.050-0.057 pixel-1) versus 0.042 ± 0.022 pixel-1 (95% CI: 0.037-0.047 pixel-1), respectively; p = 0.0001). Compressed sensing real-time cine drastically reduces arrhythmia-related artifacts and thus improves cine image quality in patients with arrhythmia.

Dataset Information

A novel single-trial event-related potential estimation method based on compressed sensing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure