A Channel Selection Method for Event Related Potential Detection based on Random Forest and Genetic Algorithm

Tang, Cong; Xu, Tao; Chen, Peng; He, Yuebang; Bezerianos, Anastasios; Wang, Hongtao

doi:10.1109/cac51589.2020.9327820

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…To verify the performance of RSBSBL, we compared the proposed method with the state-of-the-art developments in recent years on DS2, as shown in based on evolutionary computational algorithms (Kee et al, 2015;Khairullah et al, 2020;Tang et al, 2020;Martinez-Cagigal et al, 2022). The channel selection methods and classifiers used in each study are shown in the table.…”

Section: Character Recognition Performancementioning

confidence: 99%

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

Zhao

Jin

et al. 2022

Front. Hum. Neurosci.

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The P300-based brain–computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.

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Section: Character Recognition Performancementioning

confidence: 99%

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

Zhao

Jin

et al. 2022

Front. Hum. Neurosci.

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“…Many of these methods are not designed specifically for single-trial ERP estimation but for ERP detection, but they can still provide insights into our work. Tang et al [31] proposed a more advanced channel selection model combined with random forest and genetic algorithm for ERP detection. In addition to the application of the traditional machine learning method, the deep learning architectures have also been employed in ERP analysis in recent years.…”

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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Improving Multi-Class Motor Imagery EEG Signals Classification Using Ensemble Learning Method

Nugroho

Setiawan

2021

2021 9th International Conference on Information and Communication Technology (ICoICT)

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A Channel Selection Method for Event Related Potential Detection based on Random Forest and Genetic Algorithm

Cited by 4 publications

References 12 publications

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller

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

Improving Multi-Class Motor Imagery EEG Signals Classification Using Ensemble Learning Method

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