A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface

Li, Mengfan; Zhang, Pengfei; Yang, Guang; Xu, Guizhi; Miaomiao, Guo; Liao, Wenzhe

doi:10.1016/j.jneumeth.2022.109496

Cited by 14 publications

(5 citation statements)

References 36 publications

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“…The study recruited 10 healthy male volunteers in the age range of 23 to 25 years with normal vision or normal corrected vision as subjects for this experiment [18,19]. Before the experiment, subjects are informed about the experiment and signed an informed consent.…”

Section: A Participantsmentioning

confidence: 99%

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A Genetic Algorithm-Based Multi-Parameter Method for Asynchronous Working State Classification Under SSVEP

Wang,

Geng,

Duan

et al. 2024

IEEE Access

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Working state classification, which is a critical part of an asynchronous control system based on a brain-computer interface (BCI). The traditional algorithms for the working state classification are based on the threshold of power spectral density (PSD) or the canonical correlation analysis (CCA) parameters, which usually suffer insufficient robustness in case of using one threshold. To improve the robustness of working state classification, a multi-parameter asynchronous working state classification method combined with a genetic algorithm (GA-PSD-CCA) is proposed based on SSVEP. GA-PSD-CCA obtains the CCA parameter and the PSD parameter from the processed electroencephalogram (EEG) signals.Then GA-PSD-CCA iteratively searches the optimization of the PSD and CCA parameters and finds the threshold value by the genetic algorithm for the working state classification to recognize the working state. An experiment is designed to compare the application effect of GA-PSD-CCA with the traditional algorithm based on the EEG signals of ten subjects. The results show that the average accuracy with GA-PSD-CCA achieved 89.35% ± 8.39% and has an improvement of more than 7% compared with the traditional algorithm which has the highest accuracy. GA-PSD-CCA has a large improvement in asynchronous working state classification accuracy and achieves high target recognition accuracy. The BCI systems with the GA-PSD-CCA will have lower false recognition of commands and higher reliability and smoothness of state recognition processes.INDEX TERMS Asynchronous control, brain-computer interface (BCI), steady-state visual evoked potentials (SSVEP), working state classification.

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Section: A Participantsmentioning

confidence: 99%

“…Classification accuracy Acc in equation ( 16) most directly reflects the usability of the system. However, Evaluation results based on Precision and Recall are more accurate in equation (17,18). This study uses F1 as an evaluation metric to address the presence of inaccuracies in recall evaluation.…”

Section: ) Performance Evaluationmentioning

confidence: 99%

A Genetic Algorithm-Based Multi-Parameter Method for Asynchronous Working State Classification Under SSVEP

Wang,

Geng,

Duan

et al. 2024

IEEE Access

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“…Notably, FDA excels in fault isolation diagnostics, showcasing its effectiveness in identifying and isolating specific faults within a system. Conversely, PCA demonstrates its prowess in fault detection [26]- [28].…”

Section: B Literature Reviewmentioning

confidence: 99%

Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis

Mustafa,

Khan,

Samee

et al. 2023

IEEE Access

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Industrial processes are nonlinear and complicated, requiring accurate fault identification to minimize performance deterioration and respond quickly to emergencies. This work investigates industrial process defect identification and isolation, which is analytically difficult owing to their complexity. This paper carefully analyzes four design methods for flaw identification and isolation based on Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), Kernel Fisher Discriminant Analysis (KFDA), and Sequential quadratic programming (SQP). Our study includes the Tennessee Eastman Process (TEP) and the Penicillin Fermentation Process (PFP), among other comparable methods. We assess the proposed fault detection and isolation methods through detailed analysis and comparison. The simulation findings from our extensive investigation provide remarkable insights. Simulation findings show that FDA and KFDA work well in fault identification and isolation, but PCA has certain limits. We also considered SQP as a TEP fault detection and isolation improvement tool. SQP is noted for its success in nonlinear and restricted optimization problems, making it ideal for fault identification and isolation in complicated industrial processes. Data-driven design approaches increase problem identification in complicated industrial processes with greater reliability and efficiency than PCA-based methods. This study also shows that advanced data-driven techniques can improve industrial fault diagnosis, improving operational safety and system performance by leveraging the FDA, KFDA, and SQP.

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“…By comparing the actual measurements with the predicted values, the sensor error can be estimated and the data fusion algorithm can be adjusted accordingly. However, each of these methods has its advantages and limitations [14], and specific applications are influenced by specific situations and needs [15].…”

Section: Introductionmentioning

confidence: 99%

Privacy-preserving asynchronous information fusion method for AC and DC screen branch circuit sensors

Liu,

Luo,

Fei

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Due to the nonlinear property of asynchronous information between sensors, it is difficult to effectively control the data loss problem when fusing them, therefore, we propose a privacy-oriented fusion method for the asynchronous information of AC and DC screen tributary sensors. The asynchronous information characteristics of the AC/DC screen branch sensors are analyzed in terms of timestamp deviation, data consistency, data noise and interference, data discontinuity and data uncertainty, etc. With the help of nonlinear equations describing the asynchronous information of the AC/DC screen branch sensors, the original asynchronous information is fused and filtered in a recursive form, and the state vector estimates and covariance values of the asynchronous information collected by the AC/DC screen branch sensors are fused with the help of the RBF neural network. Then, the estimated values of state vector and covariance matrix are calculated in the RBF neural network to perform interactive multi-model fusion on the asynchronous information queue. In the test results, the data loss rate is stabilized within 3.0%.

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A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface

Cited by 14 publications

References 36 publications

A Genetic Algorithm-Based Multi-Parameter Method for Asynchronous Working State Classification Under SSVEP

A Genetic Algorithm-Based Multi-Parameter Method for Asynchronous Working State Classification Under SSVEP

Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis

Privacy-preserving asynchronous information fusion method for AC and DC screen branch circuit sensors

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