A Calibration-free Approach to Implementing P300-based Brain–computer Interface

Huang, Zhenjun; Guo, Jiannan; Zheng, Wenming; Wu, Yingjie; Zhang, Lin; Zheng, Huiru

doi:10.1007/s12559-021-09971-1

Cited by 10 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recently published study introduces how a linear approximation of the action-value function Q can be used to detect Chinese symbols under the P300 brain-computer interface paradigm (Huang et al, 2022). The P300 braincomputer interface paradigm uses a unique setting that requires stimulations to produce synchronization of EEG patterns.…”

Section: Approximation Of the Action-value Function Qmentioning

confidence: 99%

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Girdler

Caldbeck

Bae

2022

Front. Syst. Neurosci.

View full text Add to dashboard Cite

Creating flexible and robust brain machine interfaces (BMIs) is currently a popular topic of research that has been explored for decades in medicine, engineering, commercial, and machine-learning communities. In particular, the use of techniques using reinforcement learning (RL) has demonstrated impressive results but is under-represented in the BMI community. To shine more light on this promising relationship, this article aims to provide an exhaustive review of RL’s applications to BMIs. Our primary focus in this review is to provide a technical summary of various algorithms used in RL-based BMIs to decode neural intention, without emphasizing preprocessing techniques on the neural signals and reward modeling for RL. We first organize the literature based on the type of RL methods used for neural decoding, and then each algorithm’s learning strategy is explained along with its application in BMIs. A comparative analysis highlighting the similarities and uniqueness among neural decoders is provided. Finally, we end this review with a discussion about the current stage of RLBMIs including their limitations and promising directions for future research.

show abstract

Section: Approximation Of the Action-value Function Qmentioning

confidence: 99%

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Girdler

Caldbeck

Bae

2022

Front. Syst. Neurosci.

View full text Add to dashboard Cite

show abstract

“…P300 (Sutton et al, 1967), which is a positive peak waveform displayed at about 300 ms after being evoked by a small probability event, is one of the most studied, widely used and most prominent components of ERP (David et al, 2020;Kirasirova et al, 2020). P300 classification detection is the focus of P300-BCI research, and fast and accurate recognition is crucial to improving the performance of P300-BCI (Huang et al, 2022). The P300 usually exhibits a low signal-to-noise ratio (SNR) (Zhang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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

Li²,

Cheng³

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

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.

show abstract

“…rain-Computer Interface (BCI) bridges people with physical disabilities to the outside sensual world by converting brain signals such as electrophysiological (EEG) to control instructions [1][2][3]. Motor imagery (MI) is the mental imagination of body movement without actual muscle movement and its corresponding rhythmic activities of the brain could be observed and applied as the input signals of BCI systems [4].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

Jin

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Motor imagery-based brain-computer interfaces (MI-BCIs) features are generally extracted from a wide fixed frequency band and time window of EEG signal. The performance suffers from individual differences in corresponding time to MI tasks. In order to solve the problem, in this study, we propose a novel method named Riemannian sparse optimization and Dempster-Shafer fusion of multi-time-frequency patterns (RSODSF) to enhance the decoding efficiency. First, we effectively combine the Riemannian geometry of the spatial covariance matrix with sparse optimization to extract more robust and distinct features. Second, the Dempster-Shafer theory is introduced and used to fuse each time window after sparse optimization of Riemannian features. Besides, the probabilistic values of the support vector machine (SVM) are obtained and transformed to effectively fuse multiple classifiers to leverage potential soft information of multiple trained SVM. The openaccess BCI Competition IV dataset IIa and Competition III dataset IIIa are employed to evaluate the performance of the proposed RSODSF. It achieves higher average accuracy (89.7% and 96.8%) than state-of-the-art methods. The improvement over the common spatial patterns (SFBCSP) are respectively 9.9% and 12.4% (p<0.01, paired t-test). These results show that our proposed RSODSF method is a promising candidate for the performance improvement of MI-BCI.

show abstract

A Calibration-free Approach to Implementing P300-based Brain–computer Interface

Cited by 10 publications

References 40 publications

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

Neural Decoders Using Reinforcement Learning in Brain Machine Interfaces: A Technical Review

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

Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns

Contact Info

Product

Resources

About