Single trial estimation of event‐related potential components using spatiotemporal filtering and artificial bee colony optimized Gaussian kernel mixture model

Ranjbar, Mahdy; Mikaeili, Mohammad; Banaraki, Anahita Khorami

doi:10.1002/acs.3110

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Since there is no gold standard to compare the estimated ERP component and the original one directly, previous research has used synthesizing methods to generate EEG trials containing target ERP (sub)component(s) (Li et al 2009, Jarchi et al 2010, Ranjbar et al 2020. Such studies have assumed that the background EEG activity is similar to the white noise, and accordingly used the Gaussian probability density function to generate the temporal signals.…”

Section: Synthesizing Eeg Recordingsmentioning

confidence: 99%

ETucker: a constrained tensor decomposition for single trial ERP extraction

TaghiBeyglou

Shamsollahi

2023

Physiol. Meas.

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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 12-th order autoregressive (AR) 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.

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Section: Synthesizing Eeg Recordingsmentioning

confidence: 99%

ETucker: a constrained tensor decomposition for single trial ERP extraction

TaghiBeyglou

Shamsollahi

2023

Physiol. Meas.

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“…Some popular techniques in this group include: principal component analysis (PCA) [17,18], independent component analysis (ICA) [19,20], and sparse decomposition [21]. Recently, some methods applying improved spatiotemporal filtering [22,23] have been proposed.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Event-Related Potentials from Single-Trial EEG

Zang

Zhou

Chao

2021

Advances in Intelligent Systems and Computing

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Event-related potentials (ERPs) hidden in electroencephalogram (EEG) signals are usually estimated by a superposition and average algorithm, this algorithm makes strong assumptions on the characteristics of ERPs which are not valid. Therefore, it is desirable to discover better methods by which ERPs are estimated from single-trial EEG. In this paper, we proposed a neural network based method to estimate single-trial ERPs. Instead of recovering the ERPs, this method models the amplitudes and latencies of ERPs components. Therefore, a linear generative EEG model was used, which contains a template of ERPs local descriptors (amplitude and latency). To achieve a high accuracy, the classification model was trained by neural networks consist of 1 or 2 hidden layers. We also modified an optimization model defined in the SingleTrialEM algorithm to estimate single-trial ERPs. Then, solving the optimization problem derived from the previous classification model gave estimation of ERPs component descriptors on a single-trial basis. This method was evaluated in simulations. We compared our method with the Woody filter and a SingleTrialEM algorithm with the same simulated data. Results showed that our method is the most effective and capable of extracting ERPs in negative SNR conditions.

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“…Other novel methods have also been proposed to solve this problem. Ranjbar et al [28,29] applied an advanced spatiotemporal filtering method where the Gaussian-shaped kernels were used to represent the ERP template. Huang et al [30] proposed an ERP extraction method based on compressed sensing.…”

Section: Introductionmentioning

confidence: 99%

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation

Zang

Ding

et al. 2022

Computational and Mathematical Methods in Medicine

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Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.

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Single trial estimation of event‐related potential components using spatiotemporal filtering and artificial bee colony optimized Gaussian kernel mixture model

Cited by 4 publications

References 18 publications

ETucker: a constrained tensor decomposition for single trial ERP extraction

ETucker: a constrained tensor decomposition for single trial ERP extraction

Estimation of Event-Related Potentials from Single-Trial EEG

An EEG Classification-Based Method for Single-Trial N170 Latency Detection and Estimation

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