Subject-Independent ERP-Based Brain–Computer Interfaces

Vo, Kha; Pham, Thuy T.; Nguyen, Diep N.; Kha, Ha Hoang; Dutkiewicz, Eryk

doi:10.1109/tnsre.2018.2810332

Cited by 19 publications

(8 citation statements)

References 39 publications

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“…The SBF has been introduced for developing an early stopping method (ESM)-i.e. an automatic method that interrupts the stimulation at any point in a trial when a certain criterion, based on the ongoing classification results, is satisfied (see for instance Lenhardt et al 2008;Zhang et al Jun 2008;Liu et al 2010;Höhne et al 2010;Schreuder et al 2011;Jin et al 2011;Throckmorton et al 2013;Mainsah et al 2014;Jiang et al 2018;Vo et al 2017Vo et al , 2018Schreuder et al 2013;Kha et al 2017;Gu et al 2019;Huang et al 2020). The proposed ESM based on the SBF outperformed the current state-of-the-art early stopping methods proposed in Schreuder et al (2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving P300 Speller performance by means of optimization and machine learning

et al. 2021

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Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Improving P300 Speller performance by means of optimization and machine learning

et al. 2021

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“…We will briefly summarize both SVM and ISVM as black boxes (i.e., with just inputs and outputs without regards to the internal structure) for notational convenience. This method was reported to provide promising result promising results in the context of subject-adaptive brain-computer interface (BCI) [29][30][31][32].…”

Section: Feature Extractionmentioning

confidence: 99%

Optimal length-constrained segmentation and subject-adaptive learning for real-time arrhythmia detection

Dutkiewicz

2018

Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologi

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includes long-term rhythmic arrhythmias, i.e., bradycardia, An algorithm of data segmentation with length constraints for each segment is presented and applied in the context of arrhythmia detection. The additivity property of the cost function for each segment yields the induction proof of the exact global optimal solution. The experiments were conducted on the MIT-BIH arrhythmia dataset with the heartbeat categories recommended by the ANSI/AAMI EC57:1998 standard. The heartbeat classification task is enhanced by an adaptive learning scheme. Incremental support vector machine is used to integrate a small number of expert-annotated samples specific to the subject into the existing classifier previously learned from the dataset. The proposed segmentation scheme obtains the sensitivity of 99.89% and the positive predictivity of 99.83%. The classification sensitivities of ventricular and supraventricular detection are significantly boosted from 85.9% and 83.5% (subject-unadaptive) to 97.7% and 93.2% (subject-adaptive), respectively. Similarly the predictivities increase from 94.8% to 99.3% (ventricular), and from 67.7% to 88.0% (supraventricular) when plugging in the adaptive learning method. The signal processing framework is conducted in a simulated real-time model. As compared to the previously reported studies we achieve a competitive performance in terms of all assessment measures.

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“…It consist of three methods ICA, MNF or PCA these method improves the classification accuracy is 85% over the original data. Pham et al, [2] developed a unified classification scheme based on ensemble classifier and adaptive learning. Here using SVM method, the experimental result increasing speed and accuracy 75 to 91.26% with 12.65 iterations.…”

Section: Introductionmentioning

confidence: 99%

Back Propagation Classification of EEG for Brain-Computer Interface

Rajashekhar¹,

Anon²,

Hullamani³

et al. 2021

JNNCE Journal of Engineering and Management

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The diagnosis system presents to classify the Electroencephalogram (EEG) brain signal of patient to distinguish between normal and abnormal which are tumor and epilepsy with better classification accuracy. To design automated classification of EEG signals for the detection of normal and abnormal activities using Wavelet transform and Artificial Neural Network (ANN) Classifier is considered. Here, the system uses the back propagation with feed forward for classification which follows the ANN classification with data set training. For training, the statistical principal features will be extracted with facilitate of data base samples. The test sample is going to be classified using ANN classifier parameters and its features. The system gives better performance accuracy for different test samples

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Subject-Independent ERP-Based Brain–Computer Interfaces

Cited by 19 publications

References 39 publications

Improving P300 Speller performance by means of optimization and machine learning

Improving P300 Speller performance by means of optimization and machine learning

Optimal length-constrained segmentation and subject-adaptive learning for real-time arrhythmia detection

Back Propagation Classification of EEG for Brain-Computer Interface

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