A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Zhang, Shangen; Wang, Yijun; Zhang, Lijian; Gao, Xiaorong

doi:10.3389/fnins.2020.568000

Cited by 22 publications

(21 citation statements)

References 37 publications

(40 reference statements)

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“…The other methods are used in the same configuration for cross-subject single-experiment detection. Since the dataset is highly unbalanced [10] , we decided to use the AUC to evaluate the ERPs classification ability. In conclusion, our model achieves better performance in several models in cross-subject single-experiment scenario.…”

Section: Resultsmentioning

confidence: 99%

“…The dataset in this experiment is the RSVP-based BCI benchmark dataset [10] . 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.…”

Section: Dataset and Implementationmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Resultsmentioning

confidence: 99%

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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show abstract

“…Electrode impedance remained below 10 kΩ. Data from 64 electrodes were provided in the dataset, and we selected 62 of them (1-32, 34-42, 44-64) for further processing [24].…”

Section: Data Collectionmentioning

confidence: 99%

“…We used the BCI Controlled Robot Contest in World Robot Contest 2021 (WRC2021) data as an online dataset to compare the impact of different methods, such as xDAWN+LR, CNN, DeepConvNet [23], EEGNet, and the improved EEGNet on model performance. Additionally, we also used Tsinghua University’s A benchmark dataset for RSVP-based on BCI [24] as an offline dataset to compare the performance of these models. The results showed that in recognition of target images, the improved EEGNet model could achieve a higher recall rate and better solve the classification problem of the RSVP paradigm.…”

Section: Introductionmentioning

confidence: 99%

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

Zhang

Wang

et al. 2022

Brain Science Advances

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As a new type of brain–computer interface (BCI), the rapid serial visual presentation (RSVP) paradigm has attracted significant attention. The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition. This paper proposed an improved EEGNet model to achieve good performance in offline and online data. Specifically, the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram (EEG) signals. The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples. Additionally, the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model. We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place. The average recall rate of the four participants reached 51.56% in triple classification. In the offline data benchmark dataset (64 subjects-RSVP tasks), the average recall rates of groups A and B reached 76.07% and 78.11%, respectively. We provided an alternative method to identify targets based on the RSVP paradigm.

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“…Simultaneously, HDCA is the baseline method for the classification in RSVP [2]. Inspired by these methods, we propose a channel selection method, called SparseEA-HDCA, that combined SparseEA with HDCA and tested the proposed methods on the published RSVP benchmark dataset [25]. The optimized algorithm's specific channel combination model was transferred to other untrained subject, then to solve poor cross-subject classification problems of the existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Xu¹,

Chen²,

Wang³

et al. 2021

J. Neural Eng.

Self Cite

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Objective. Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical applications. Therefore, it is necessary to reduce the number of channels to enhance the classification performance and users experience. Furthermore, cross-subject generalization has always been one of major challenges in electroencephalography channel reduction, especially in the RSVP paradigm. Most search-based channel selection method presented in the literature are single-objective methods, the classification accuracy (ACC) is usually chosen as the only criterion. Approach. In this article, the idea of multi-objective optimization was introduced into the RSVP channel selection to minimize two objectives: classification error and the number of channels. By combining a multi-objective evolutionary algorithm for solving large-scale sparse problems and hierarchical discriminant component analysis (HDCA), a novel channel selection method for RSVP was proposed. After that, the cross-subject generalization validation through the proposed channel selection method. Main results. The proposed method achieved an average ACC of 95.41% in a public dataset, which is 3.49% higher than HDCA. The ACC was increased by 2.73% and 2.52%, respectively. Besides, the cross-subject generalization models in channel selection, namely special-16 and special-32, on untrained subjects show that the classification performance is better than the Hoffmann empirical channels. Significance. The proposed channel selection method could reduce the calibration time in the experimental preparation phase and obtain a better accuracy, which is promising application in the RSVP scenario that requires low-density electrodes.

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A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Cited by 22 publications

References 37 publications

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

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

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

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

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