A comparison study of two P300 speller paradigms for brain–computer interface

Pan, Jiahui; Li, Yuanqing; Gu, Zhenghui; Yu, Zhuliang

doi:10.1007/s11571-013-9253-1

Cited by 41 publications

(30 citation statements)

References 30 publications

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“…Various neural activities can be used as features in electroencephalogram (EEG)-based BCI systems. P300 evoked potentials [5][6][7][8][9][10], slow cortical potentials, steady state visually evoked potentials [11,12], and event-related desynchronization (ERD) [ [14] are extensively used in BCI systems.…”

Section: Introductionmentioning

confidence: 99%

Improved SFFS method for channel selection in motor imagery based BCI

et al. 2016

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Section: Introductionmentioning

confidence: 99%

Improved SFFS method for channel selection in motor imagery based BCI

et al. 2016

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“…When the target character's row or column was flashing, a positive potential (called P300 ERP) related to the event could be detected in the subject's scalp (about 300 ms after receiving the stimulus); if not, the detected EEG data were non-P300 event-related potentials (N-P300 ERP) [11]. In addition to this standard speller system, there are other paradigms, such as row-column (RC) paradigm [12], single character (SC) paradigm [13], region-based(RB) paradigm [14], and so on. For all these systems, how to identify quickly and accurately is critical to improving the performance of BCI systems.…”

Section: Introductionmentioning

confidence: 99%

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Wang

et al. 2020

Applied Sciences

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Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain–computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to improve the traditional convolutional neural network framework, which can increase the network depth and improve the network’s ability to classify P300 electroencephalogram signals. The proposed algorithm was evaluated by two datasets (the dataset from the competition and the dataset from the laboratory). The results show that, in the dataset I, the proposed P300 classification algorithm could obtain accuracy rates higher than 95%, and achieve one of the best performances in four classification algorithms, while, in the dataset II, the proposed P300 classification algorithm can get accuracy rates higher than 90%, and is superior to the other three algorithms in all ten subjects. These demonstrated the effectiveness of the proposed algorithm. The proposed classification algorithm can be applied in the actual brain–computer interface systems to help people with disability in the daily lives.

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“…MI-based BCI systems utilize the brain activity associated with imagined motor movements as control commands for external devices (He, Baxter, Edelman, Cline, & Ye, 2015;Onose, Grozea, et al, 2012). These systems operate without external stimulus and thus are more easily used than stimuli-based BCIs (Wolpaw, & Wolpaw, 2012;Pan, Li, Gu, & Yu, 2013). Upon imagining movement, the rhythmic activities of the brain observed in the mu and beta rhythms are suppressed contralaterally.…”

Section: Introductionmentioning

confidence: 99%

Towards correlation-based time window selection method for motor imagery BCIs

et al. 2018

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The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs.

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A comparison study of two P300 speller paradigms for brain–computer interface

Cited by 41 publications

References 30 publications

Improved SFFS method for channel selection in motor imagery based BCI

Improved SFFS method for channel selection in motor imagery based BCI

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Towards correlation-based time window selection method for motor imagery BCIs

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