A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

Song, Xiyu; Zeng, Ying; Li, Tong; Shu, Jun; Qiang, Yang; Kou, Jian; Minghua, Sun; Yan, Bin

doi:10.1155/2022/4752450

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Single-person BCI system’s performance is subject to individual differences between users and their physical or mental conditions, and this weakness becomes more prominent as BCI system develops further ( Song et al, 2022 ). In contrast, multi-person-coordinated BCI can better serve the future socialized human–computer interaction and will most certainly dominate this field both in terms of research and application.…”

Section: Introductionmentioning

confidence: 99%

Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

Su²,

Belkacem³

et al. 2022

Front. Neurosci.

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ObjectiveThe conventional single-person brain–computer interface (BCI) systems have some intrinsic deficiencies such as low signal-to-noise ratio, distinct individual differences, and volatile experimental effect. To solve these problems, a centralized steady-state visually evoked potential collaborative BCI system (SSVEP-cBCI), which characterizes multi-person electroencephalography (EEG) feature fusion was constructed in this paper. Furthermore, three different feature fusion methods compatible with this new system were developed and applied to EEG classification, and a comparative analysis of their classification accuracy was performed with transfer learning-based convolutional neural network (TL-CNN) approach.ApproachAn EEG-based SSVEP-cBCI system was set up to merge different individuals’ EEG features stimulated by the instructions for the same task, and three feature fusion methods were adopted, namely parallel connection, serial connection, and multi-person averaging. The fused features were then input into CNN for classification. Additionally, transfer learning (TL) was applied first to a Tsinghua University (THU) benchmark dataset, and then to a collected dataset, so as to meet the CNN training requirement with a much smaller size of collected dataset and increase the classification accuracy. Ten subjects were recruited for data collection, and both datasets were used to gauge the three fusion algorithms’ performance.Main resultsThe results predicted by TL-CNN approach in single-person mode and in multi-person mode with the three feature fusion methods were compared. The experimental results show that each multi-person mode is superior to single-person mode. Within the 3 s time window, the classification accuracy of the single-person CNN is only 90.6%, while the same measure of the two-person parallel connection fusion method can reach 96.6%, achieving better classification effect.SignificanceThe results show that the three multi-person feature fusion methods and the deep learning classification algorithm based on TL-CNN can effectively improve the SSVEP-cBCI classification performance. The feature fusion method of multi -person parallel feature connection achieves better classification results. Different feature fusion methods can be selected in different application scenarios to further optimize cBCI.

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Section: Introductionmentioning

confidence: 99%

Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

Su²,

Belkacem³

et al. 2022

Front. Neurosci.

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“…In the context of decision-making, BCIs have demonstrated to be a critical tool to improve the correctness of our decisions. For example, BCIs can decode the decision of multiple participants with better accuracy and faster than single non-BCI users [21]- [23]. Moreover, this collaborative BCI approach has also been applied to traditional BCI paradigms, such as motor imagery [24], [25], to boost performance.…”

Section: Optimal Brain-computer Interfacesmentioning

confidence: 99%

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

Salvatore

Valeriani²,

Piccialli

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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The aim of this study is to maximize group decision performance by optimally adapting EEG confidence decoders to the group composition. We train linear support vector machines to estimate the decision confidence of human participants from their EEG activity. We then simulate groups of different size and membership by combining individual decisions using a weighted majority rule. The weights assigned to each participant in the group are chosen solving a small-dimension, mixed, integer linear programming problem, where we maximize the group performance on the training set. We therefore introduce optimized collaborative brain-computer interfaces (BCIs), where the decisions of each team member are weighted according to both the individual neural activity and the group composition. We validate this approach on a face recognition task undertaken by 10 human participants. The results show that optimal collaborative BCIs significantly enhance team performance over other BCIs, while improving fairness within the group. This research paves the way for practical applications of collaborative BCIs to realistic scenarios characterized by stable teams, where optimizing the decision policy of a single group may lead to significant long-term benefits of team dynamics.

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“…Zheng et al (2020) introduced an crosssession EEG dataset to improve the performance and utility of a collaborative RSVP-based BCI system. Song et al (2022) proposed a Mutual Learning Domain Adaptation Network (MLDANet) cBCI framework with information interaction, dynamic learning, and individual transfer capabilities that exhibited superior population detection performance. Li P. et al (2022) applied migration learning-based CNNs to steady-state visual evoked potentials (SSVEP).…”

Section: Introductionmentioning

confidence: 99%

“…A series of experiments (Wang and Jung, 2011;Li et al, 2020, Li P. et al, 2022Song et al, 2022) demonstrate that centralized cBCI improves overall BCI performance by fusing data from multiple subjects. To further improve the accuracy of single-trial classification, this paper combines a combination of centralized cBCI data fusion and CNN classification algorithm to identify single-trial P300.…”

Section: Introductionmentioning

confidence: 99%

Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN

Li²,

Cheng³

et al. 2023

Front. Neurosci.

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IntroductionCurrently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals.MethodsIn this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification.ResultsIn this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms.DiscussionThe results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.

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A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

Cited by 6 publications

References 35 publications

Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

Optimized Collaborative Brain-Computer Interfaces for Enhancing Face Recognition

Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN

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