P300 Detection with Brain–Computer Interface Application Using PCA and Ensemble of Weighted SVMs

Kundu, Sourav; Ari, Samit

doi:10.1080/03772063.2017.1355271

Cited by 51 publications

(24 citation statements)

References 32 publications

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“…When the character identification rate reaches this level of performance, the usage of word predicting algorithms allows to implement practical speller applications. Results for this competition have shown perfect classification with tailored algorithms [ 82 ]. This level is also similar to the performance obtained for the Experiment 3, which represents coincidentally the more realistic scenario for the pseudo-real dataset.…”

Section: Discussionmentioning

confidence: 99%

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018

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The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

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Section: Discussionmentioning

confidence: 99%

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

2018

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“…One way to conduct undersampling in the context of classification is to divide the majority class data into multiple subsets, build classifiers for individual subsets, and combine classification outcomes. For instance, in Kundu and Ari’s study [ 27 ], the majority class data were divided into multiple subsets, and SVM model was built for each subset. Then, they combined the classification output of each classifier in each subset by assigning weights to those outputs according to the cross-validation results; a more non-negative weight for higher cross-validation accuracy with the sum of the weights equal to 1.…”

Section: Methodsmentioning

confidence: 99%

Improvement of P300-Based Brain–Computer Interfaces for Home Appliances Control by Data Balancing Techniques

Lee

Kim

2020

Sensors

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The oddball paradigm used in P300-based brain–computer interfaces (BCIs) intrinsically poses the issue of data imbalance between target stimuli and nontarget stimuli. Data imbalance can cause overfitting problems and, consequently, poor classification performance. The purpose of this study is to improve BCI performance by solving this data imbalance problem with sampling techniques. The sampling techniques were applied to BCI data in 15 subjects controlling a door lock, 15 subjects an electric light, and 14 subjects a Bluetooth speaker. We explored two categories of sampling techniques: oversampling and undersampling. Oversampling techniques, including random oversampling, synthetic minority oversampling technique (SMOTE), borderline-SMOTE, support vector machine (SVM) SMOTE, and adaptive synthetic sampling, were used to increase the number of samples for the class of target stimuli. Undersampling techniques, including random undersampling, neighborhood cleaning rule, Tomek’s links, and weighted undersampling bagging, were used to reduce the class size of nontarget stimuli. The over- or undersampled data were classified by an SVM classifier. Overall, some oversampling techniques improved BCI performance while undersampling techniques often degraded performance. Particularly, using borderline-SMOTE yielded the highest accuracy (87.27%) and information transfer rate (8.82 bpm) across all three appliances. Moreover, borderline-SMOTE led to performance improvement, especially for poor performers. A further analysis showed that borderline-SMOTE improved SVM by generating more support vectors within the target class and enlarging margins. However, there was no difference in the accuracy between borderline-SMOTE and the method of applying the weighted regularization parameter of the SVM. Our results suggest that although oversampling improves performance of P300-based BCIs, it is not just the effect of the oversampling techniques, but rather the effect of solving the data imbalance problem.

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“…Kundu and Sourav used PCA to reduce the dimensionality of P300 signals, and then used SVM to classify the reduced-dimensional signals. PCA reduced the computational burden of weighted classifiers and speeds up the classification speed [19]. Like combined multi-scale filters and PCA to classify EEG signals, the classification accuracy can reach 91.13% [20].…”

Section: Introductionmentioning

confidence: 99%

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

Wang

et al. 2020

Applied Sciences

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Aiming at enhancing the classification accuracy of P300 Electroencephalogram signals in a non-invasive brain–computer interface system, a novel P300 electroencephalogram signals classification algorithm is proposed which is based on improved convolutional neural network. In the data preprocessing part, the proposed P300 classification algorithm used the Principal Component Analysis algorithm to not only remove the noise and artifacts in the data, but also increase the data processing speed. Furthermore, the proposed P300 classification algorithm employed the parallel convolution method to improve the traditional convolutional neural network framework, which can increase the network depth and improve the network’s ability to classify P300 electroencephalogram signals. The proposed algorithm was evaluated by two datasets (the dataset from the competition and the dataset from the laboratory). The results show that, in the dataset I, the proposed P300 classification algorithm could obtain accuracy rates higher than 95%, and achieve one of the best performances in four classification algorithms, while, in the dataset II, the proposed P300 classification algorithm can get accuracy rates higher than 90%, and is superior to the other three algorithms in all ten subjects. These demonstrated the effectiveness of the proposed algorithm. The proposed classification algorithm can be applied in the actual brain–computer interface systems to help people with disability in the daily lives.

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P300 Detection with Brain–Computer Interface Application Using PCA and Ensemble of Weighted SVMs

Cited by 51 publications

References 32 publications

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

Improvement of P300-Based Brain–Computer Interfaces for Home Appliances Control by Data Balancing Techniques

A Novel P300 Classification Algorithm Based on a Principal Component Analysis-Convolutional Neural Network

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