Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

Arjona, Cristian; Pentácolo, José; Gareis, Iván; Atum, Yanina; Gentiletti, Gerardo Gabriel; Acevedo, Rubén; Rufiner, Hugo Leonardo

doi:10.1088/1742-6596/332/1/012025

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Area Under Curve (AUC) [57] Calculated by mean and variance Area under ROC curve, generally between 0.5 and 1…”

Section: Performance Evaluation Methods For General Bci Systemmentioning

confidence: 99%

“…(6) How to evaluate the performance of BCI customized for specific users Although there are some criteria for evaluating or reporting BCI performance [57], mainly from the technical perspective, BCI is directly controlled by a specific user's brain signals to improve the user's work efficiency and quality of life. Therefore, it is also necessary to combine user-centered BCI evaluation methods to develop a BCI that the user is satisfied with.…”

Section: Challenges Faced By Personalized Bcimentioning

confidence: 99%

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Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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Brain–computer interfaces (BCIs) are a new technology that subverts traditional human–computer interaction, where the control signal source comes directly from the user’s brain. When a general BCI is used for practical applications, it is difficult for it to meet the needs of different individuals because of the differences among individual users in physiological and mental states, sensations, perceptions, imageries, cognitive thinking activities, and brain structures and functions. For this reason, it is necessary to customize personalized BCIs for specific users. So far, few studies have elaborated on the key scientific and technical issues involved in personalized BCIs. In this study, we will focus on personalized BCIs, give the definition of personalized BCIs, and detail their design, development, evaluation methods and applications. Finally, the challenges and future directions of personalized BCIs are discussed. It is expected that this study will provide some useful ideas for innovative studies and practical applications of personalized BCIs.

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“…Area Under Curve (AUC) [57] Calculated by mean and variance Area under ROC curve, generally between 0.5 and 1…”

Section: Performance Evaluation Methods For General Bci Systemmentioning

confidence: 99%

Section: Challenges Faced By Personalized Bcimentioning

confidence: 99%

Personalized Brain–Computer Interface and Its Applications

Gong

Nan

et al. 2022

JPM

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“…An ensemble SWLDA classifier was first proposed by Johnson et al and evaluated on their own P300-based BCI data (6480 training ERP data) [27] . Arjona et al evaluated a variety of ensemble LDA classifiers using 3024 training data [28] .…”

Section: Introductionmentioning

confidence: 99%

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

Onishi

Natsume

2014

PLoS ONE

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A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.

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“…Ensembled Learning improves classification accuracy and stability by combining the strengths of multiple models. The most widely used ensemble learning methods such as Bagging [24],…”

Section: Introductionmentioning

confidence: 99%

Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition

Meng,

Zhu,

et al. 2024

Preprint

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Same limb motor imagery (MI) brain-computer interfaces can effectively overcome the cognitive disassociation problem of the traditional different-limb MI paradigm, and they can reduce the patient burden and extend the functionality of external devices more effectively. However, the electroencephalogram (EEG) MI features of same limb originate from one side of the brain, which poses a great challenge to MI EEG feature mining and selection as well as accurate decoding. To overcome this problem, we propose an adaptive feature selection strategy for subject-specific optimal frequency band based on regularized common spatial pattern (RCSP) and stepwise discriminant analysis, then combine the integrated classification strategy to accurately decode three types of single-limb MI tasks. As there are minor frequency band differences and huge variability for the same limb MI tasks, the optimal frequency band range for each subject was selected by stepwise discriminant analysis, and RCSP was used to extract spatial distribution features, which reduced the influence of the length of the time window and differences of the frequency bands. Then an integrated classification strategy based on multiple efficient classifiers is used for MI accurate recognition. The proposed method obtains 76.58% accuracy in the unilateral limb MI recognition task, which is 12.67%, 9.89%, 6.62%, and 7.90% higher than other traditional decoding methods such as CSP + LDA, FBCSP + LDA, FBCSP + C2CM, and FBCSP + SVM, respectively. Compared with Deep ConvNet and EEGNet, the decoding accuracy is improved by 16.93% and 7.33%, respectively. The experimental results show that our proposed highly efficient method improves the decoding accuracy for classifying different joints of unilateral limbs and has high promotion and application value.

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Evaluation of LDA Ensembles Classifiers for Brain Computer Interface

Cited by 4 publications

References 10 publications

Personalized Brain–Computer Interface and Its Applications

Personalized Brain–Computer Interface and Its Applications

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

Stepwise Discriminant Analysis based Optimal Frequency Band Selection and Ensemble Learning for Same Limb MI Recognition

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