Applying dynamic data collection to improve dry electrode system performance for a P300-based brain–computer interface

2017 11th International Symposium on Medical Information and Communication Technology (ISMICT)

et al. 2017

Abstract-In brain-computer interface (BCI) research, there must be a trade-off between accuracy and speed of the BCI system, especially those based on event-related potentials (ERPs). This paper proposes a novel method which can significantly increase the spelling bit rate while also maintaining the desired accuracy. We provide an adaptive real-time stopping method based on the scores of ensemble support vector machine classifiers. We apply a criteria assessment process on the classifiers' scores to dynamically stop the ERP-evoked paradigms at any flashing sequence. Our experiments were conducted on three different P300-Speller data sets (BCI Competition II, BCI Competition III and Akimpech). Our proposed framework significantly outperformed the related state-of-the-art studies in terms of character output accuracy and elicitation bit rate rise between static and dynamic stopping schemes. We improve the average bit rate by over 80% while perfectly maintaining the best original static accuracy of over 96%.

Section: Resultsmentioning

confidence: 95%

Dynamic stopping using eSVM scores analysis for event-related potential brain-computer interfaces

2017 11th International Symposium on Medical Information and Communication Technology (ISMICT)

et al. 2017

“…In this paper, we select support vector machine (SVM) as the baseline learning algorithm, which had been used in various BCI studies [22]- [25]. The reason of choosing SVM, rather than some Bayesian probabilistic approaches [16], [18], [26]- [28], is that we want to exploit the computational strength to combine the dynamic stopping check with the adaptive SVM update algorithm [29], [30] into the existing ensemble partitioning framework. This integration can significantly improve the performance as compared to other methods, which will be presented in Section V.…”

Section: B Dataset Partitioningmentioning

confidence: 99%

“…Mainsah [17] scheme into P3S and conducted the experiments on 10 ALS subjects. Clements [18] analyzed the effects of wet and dry sensors on 8 subjects with communication difficulties. Although different in their frameworks, all of those mentioned works shared a common result that the accuracy performing on ALS or disabled subjects was much lower than those of healthy ones.…”

Section: Introductionmentioning

confidence: 99%

Subject-Independent ERP-Based Brain–Computer Interfaces

Pham

IEEE Trans. Neural Syst. Rehabil. Eng.

et al. 2018

Brain-computer interfaces (BCIs) are desirable for people to express their thoughts, especially those with profound disabilities in communication. The classification of brain patterns for each different subject requires an extensively time-consuming learning stage specific to that person, in order to reach satisfactory accuracy performance. The training session could also be infeasible for disabled patients as they may not fully understand the training instructions. In this paper, we propose a unified classification scheme based on ensemble classifier, dynamic stopping, and adaptive learning. We apply this scheme on the P300-based BCI, with the subject-independent manner, where no learning session is required for new experimental users. According to our theoretical analysis and empirical results, the harmonized integration of these three methods can significantly boost up the average accuracy from 75.00% to 91.26%, while at the same time reduce the average spelling time from 12.62 to 6.78 iterations, approximately to two-fold faster. The experiments were conducted on a large public dataset which had been used in other related studies. Direct comparisons between our work with the others' are also reported in details.

“…The other works which were mentioned in the literature [1], [10], [11], [21], [30] were not implemented on the Akimpech dataset, as well as not on the subject-independent basis. However, they all have the same dynamic stopping method of Bayesian approach, which was first proposed by [30].…”

Section: F Comparison With Related Studiesmentioning

confidence: 99%

“…Mainsah [1], [10], Clements [21], Kindermans [11], [22] all used the similar DS method on different signal processing frameworks. They implement the DS by updating the posterior probabilities of each letter via a Bayesian interference scheme after each iteration, given the classifier score history of the previous iterations.…”

Section: Introductionmentioning

confidence: 99%

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

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

et al. 2017

Abstract-Brain-computer interfaces (BCIs) are used to assist people, especially those with verbal or physical disabilities, communicate with the computer to indicate their selections, control a device or answer questions only by their mere thoughts. Due to the noisy nature of brain signals, the required time for each experimental session must be lengthened to reach satisfactory accuracy. This is the trade-off between the speed and the precision of a BCI system. In this paper, we propose a unified method which is the integration of ensemble classifier, dynamic stopping, and adaptive learning. We are able to both increase the accuracy, as well as to reduce the spelling time of the P300-Speller. Another merit of our study is that it does not require the training phase for any new subject, hence eliminates the extensively time-consuming process for learning purposes. Experimental results show that we achieve the averaged bit rate boost up of 182% on 15 subjects. Our best achieved accuracy is 95.95% by using 7.49 flashing iterations and our best achieved bit rate is 40.87 bits/min with 83.99% accuracy and 3.64 iterations. To the best of our knowledge, these results outperformed most of the related P300-based BCI studies.