Subject-Independent P300 BCI Using Ensemble Classifier, Dynamic Stopping and Adaptive Learning

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

doi:10.1109/glocom.2017.8255030

Cited by 8 publications

(3 citation statements)

References 28 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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“…The results were found between 71 and 96.5% in 5–15 trials. Various optimisation approaches were also adopted, such as the ensemble of support vector machine (SVM) [8 ] and dynamic stopping [9 ] to reduce the trade‐off between detection time and accuracy. The deep learning employing a convolution neural network (CNN) is found to have outperformed traditional machine learning methods due to the ability to eliminate hierarchical features and restore spatial data.…”

Section: Related Researchmentioning

confidence: 99%

Brain–computer interface‐based single trial P300 detection for home environment application

2020

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P300 speller‐based brain–computer interface (BCI) is an immediate correspondence between the human brain and computer that depends on the translation of mind reactions produced by the stimulus of a subject utilising the P300 speller. No muscle movements are required for this communication. As a P300 paradigm, a novel 2 × 3 matrix consisting of visual home appliances is proposed, which helps disabled people ease their lives by accessing mobile, light, fan, door, television, electric heater etc. In most of the current P300‐based BCIs, 5–15 trials work better and the low information transfer rate (ITR) is a major issue in its adaptation in real‐time. The objective of this Letter is to improve accuracy as well as an ITR for real‐time home appliance control applications. To address this, the authors proposed a single trial weighted ensemble of compact convolution neural network and obtained an ITR of 46.45 bits per minute and an average target appliance accuracy of 93.22% for the BCI‐based home environment system. The experimental findings confirmed the feasibility of the proposed method and thus can provide guidance for future use of the system for paralysed patients.

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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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Subject-Independent P300 BCI Using Ensemble Classifier, Dynamic Stopping and Adaptive Learning

Cited by 8 publications

References 28 publications

Improving P300 Speller performance by means of optimization and machine learning

Improving P300 Speller performance by means of optimization and machine learning

Brain–computer interface‐based single trial P300 detection for home environment application

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

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