Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs

Xu, Meng; Chen, Yuanfang; Wang, Dan; Wang, Yijun; Zhang, Lijian; Wei, Xiaoqian

doi:10.1088/1741-2552/ac0489

Cited by 11 publications

(8 citation statements)

References 48 publications

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“…To enhance the performance of the algorithm further, this study incorporated the channel selection module and data augmentation methods proposed by the research team in earlier work by Xu et al into the preprocessing stage of the classification model [31,32]. The channel selection module uses the SparseEA algorithm mentioned above, which is a multi-objective channel selection approach that is based on large-scale sparse evolution.…”

Section: Channel Selectionmentioning

confidence: 99%

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

Zhang,

Xu,

Zhang

et al. 2024

Bioengineering

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The rapid serial visual presentation-based brain–computer interface (RSVP-BCI) system achieves the recognition of target images by extracting event-related potential (ERP) features from electroencephalogram (EEG) signals and then building target classification models. Currently, how to reduce the training and calibration time for classification models across different subjects is a crucial issue in the practical application of RSVP. To address this issue, a zero-calibration (ZC) method termed Attention-ProNet, which involves meta-learning with a prototype network integrating multiple attention mechanisms, was proposed in this study. In particular, multiscale attention mechanisms were used for efficient EEG feature extraction. Furthermore, a hybrid attention mechanism was introduced to enhance model generalization, and attempts were made to incorporate suitable data augmentation and channel selection methods to develop an innovative and high-performance ZC RSVP-BCI decoding model algorithm. The experimental results demonstrated that our method achieved a balance accuracy (BA) of 86.33% in the decoding task for new subjects. Moreover, appropriate channel selection and data augmentation methods further enhanced the performance of the network by affording an additional 2.3% increase in BA. The model generated by the meta-learning prototype network Attention-ProNet, which incorporates multiple attention mechanisms, allows for the efficient and accurate decoding of new subjects without the need for recalibration or retraining.

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Section: Channel Selectionmentioning

confidence: 99%

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

Zhang,

Xu,

Zhang

et al. 2024

Bioengineering

Self Cite

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“…The original dataset was first preprocessed by removing the bad channels EOG1 and EOG2; band-pass filtering operation of the data from 2Hz to 30Hz; data segmentation from 200ms before stimulus onset to 1000ms after stimulus onset (-200~1000ms) with baseline correction (-200~0ms); and data resampling to 128Hz. In order to reduce data redundancy and improve training efficiency, this study chose to use the Special-16 electrode combination [11] for subsequent experiments.…”

Section: Dataset and Implementationmentioning

confidence: 99%

An attention-based deep convolutional recurrent model for single trial subject-independent P300 detection

Ran¹,

Huang²,

Lang³

2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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Deep learning has recently become very popular in various fields, and CNN and RNN are two very important elements in deep learning. They perform very well in the task of detecting ERPs (Event-Related Potentials) on EEG. Since EEG data contains many defects, such as huge variability among different people and low signal-to-noise ratio, it is very difficult to develop a system generalizable between subjects is still difficult. In this paper, we examined a deep learning Model (ADCRM) for EEG-based ERPs detection in subject-independent P300 detection scenario. In our model, the convolutional network module is constructed for encode the frequency domain and spatial characteristics of EEG signals and the recurrent network module is built for exploring the dynamic time domain characteristics, while a channel-wise attention module is integrated to focus on the most important part of the EEG channels. We trained the model with a relatively small number of datasets and examined its performance by comparing with the previously proposed methods. The experiment result shows that our model achieved the best performance among all the models we tested. This result indicates that our model can find the deep-seated EEG features and generalize to other subjects.

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“…Channel set 2 and channel set 5 consisted of 32 and 8 channels, according to [38], respectively. Channel set 3 consisted of 16 channels, according to [39]. Channel set 4 consisted of 8 channels distributed on the centerline of the brain topography.…”

Section: Channel Selectionmentioning

confidence: 99%

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

Lian,

Qiao,

Zhao

et al. 2023

Brain Sciences

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Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain–computer interfaces in EEG-based target detection areas.

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Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs

Cited by 11 publications

References 48 publications

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

Attention-ProNet: A Prototype Network with Hybrid Attention Mechanisms Applied to Zero Calibration in Rapid Serial Visual Presentation-Based Brain–Computer Interface

An attention-based deep convolutional recurrent model for single trial subject-independent P300 detection

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

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