Modeling Strategies and Spatial Filters for Improving the Performance of P300-speller within and across Individuals

Wang, Tao; Li, Xiuting; An, Xingwei; Ke, Yufeng; Xu, Jinzhao; Xu, Min; Kong, Linghan; Liu, Wentao; Ming, Dong

doi:10.1109/civemsa45640.2019.9071607

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…[17]- [19]. Because such transfer-learning approaches typically deal with large datasets, the proposed reformulation could significantly reduce the number of matrix multiplications (i.e., computational time) required to solve TRCA.…”

Section: Computer Simulationmentioning

confidence: 99%

Reformulating Task-Related Component Analysis for Reducing its Computational Complexity

Chiang¹,

Wong²,

Wan³

et al. 2022

Preprint

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Section: Computer Simulationmentioning

confidence: 99%

Reformulating Task-Related Component Analysis for Reducing its Computational Complexity

Chiang¹,

Wong²,

Wan³

et al. 2022

Preprint

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“…The current mainstream P3000 detection approaches can be categorized into two types: deep learning (DL) and traditional technologies using statistical features and classifiers. In the traditional ones, the feature extraction mainly includes measures such as independent component analysis (ICA) [12], canonical correlation analysis (CCA) [13], common spatial patterns (CSP) [14], and XDAWN spatial filter [15]. Commonly used classifiers include linear discriminant analysis (LDA) [16], support vector machine (SVM) [17], and Riemannian geometry classifier (RGC) [18], among others.…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Neural Network for P300 Detection

et al. 2021

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P300 spellers are common brain-computer interface (BCI) systems designed to transfer information between human brains and computers. In most P300 detections, the P300 signals are collected by averaging multiple electroencephalographic (EEG) changes to the same target stimuli, so the participants are obliged to endure multiple repeated stimuli. In this study, a spatial-temporal neural network (STNN) based on deep learning (DL) is proposed for P300 detection. It detects P300 signals by combining the outputs from a temporal unit and a spatial unit. The temporal unit is a flexible framework consisting of several temporal modules designed for analyzing brain potential changes in the time domain. The spatial unit combines one-dimensional convolutions (Conv1Ds) and linear layers to generalize P300 features from the space domain, and it can decode EEG signals recorded using different numbers of electrodes. Both amyotrophic lateral sclerosis (ALS) patients and healthy subjects can benefit from this study. In the withinsubject P300 detection and the cross-subject P300 detection, our approach gained higher performance with fewer repeated stimuli than other comparative approaches. Furthermore, we applied the proposed STNN in the P300 detection challenge of BCI Competition III. The accuracy score was 89% in the fifth round of repeated stimuli, outperforming the best result in the literature (accuracy = 80%) to the best of our knowledge. The results demonstrate that the proposed STNN performs well with limited stimuli and is robust enough for various P300 detections.INDEX TERMS P300 detection, spatial-temporal neural network (STNN), deep learning (DL).

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Transfer Learning for P300 Speller: An Alignment Approach for Cross-Subject P300 Data

Liu,

Ahmad,

Beyette

2024

SoutheastCon 2024

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Modeling Strategies and Spatial Filters for Improving the Performance of P300-speller within and across Individuals

Cited by 3 publications

References 9 publications

Reformulating Task-Related Component Analysis for Reducing its Computational Complexity

Reformulating Task-Related Component Analysis for Reducing its Computational Complexity

Spatial-Temporal Neural Network for P300 Detection

Transfer Learning for P300 Speller: An Alignment Approach for Cross-Subject P300 Data

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