P300 Based Brain-Computer Interface Using Hidden Markov Models

Helmy, Salah; Al-Ani, Tarik; Hamam, Yskandar; El-Madbouly, E.

doi:10.1109/issnip.2008.4761974

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…This classifier has been applied, with success, to a relatively large number of BCIs problems (Blankertz et al, 2002;Garrett et al, 2003;Rakotomamonjy et al, 2005;Helmy et al, 2008;Trad et al, 2009). …”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…Furthermore, they are generative, which enables them to perform more efficient rejection of uncertain samples than discriminative classifiers. As brain signals used to drive BCI have specific time courses, HMM have been applied to the classification of temporal sequences of BCI features or even raw EEG (Obermeier et al, 2000;Cincotti et al, 2003;Schlogl et al, 2005;Helmy et al, 2008;Trad et al, 2009). HMMs are very efficient nonlinear techniques used for the classification of time series (Rabiner, 1989).…”

Section: Nonlinear Bayesian Classifiers (Nbc)mentioning

confidence: 99%

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Signal Processing and Classification Approaches for Brain-Computer Interface

Al-Ani¹,

Trad²

2010

Intelligent and Biosensors

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Nonlinear Bayesian Classifiers (Nbc)mentioning

confidence: 99%

Signal Processing and Classification Approaches for Brain-Computer Interface

Al-Ani¹,

Trad²

2010

Intelligent and Biosensors

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“…Many of these classifiers are based on liner models, such as variations of linear discriminant analysis (LDA), principal component analysis (PCA), and support vector machines (SVMs) -including non-linear kernel SVMs [7], [8]. Bayesian approaches as well as clustering techniques, ensemble learning such as random forests (RF) and gradient boosting (GB), hidden Markov models (HMMs), and distance metrics are also arXiv:2301.12322v1 [cs.LG] 29 Jan 2023 considered [9], [10], [11], [12], [13], [14], [15]. Most of these approaches include some degree of feature engineering.…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface

Mello

Weingart

Rudd³

2022

2022 26th International Conference on Pattern Recognition (ICPR)

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Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the field, however, at present consumer and clinical viability remains low. A key reason for this is that many of the existing BCI deployments require substantial data collection per end-user, which can be cumbersome, tedious, and error-prone to collect. We address this challenge via a deep learning model, which, when trained across sufficient data from multiple subjects, offers reasonable performance out-of-the-box, and can be customized to novel subjects via a transfer learning process. We demonstrate the fundamental viability of our approach by repurposing an older but well-curated electroencephalography (EEG) dataset and benchmarking against several common approaches/techniques. We then partition this dataset into a transfer learning benchmark and demonstrate that our approach significantly reduces data collection burden per-subject. This suggests that our model and methodology may yield improvements to BCI technologies and enhance their consumer/clinical viability.

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“…Recently, HMMs represent in a natural way the individual component states of a dynamic system. This fact make them useful in biomedical signal analysis and in medical diagnosis Al-ani & Trad, 2010a;Daidone et al, 2006;Helmy et al, 2008;Novák et al, 2004a;c;d). For the same reason, they are used in fault detection and mechanical system monitoring (Al-ani & Hamam, 2006;Bunks et al, 2000;Heck & McClellan, 1991;Miao et al, 2007;Smyth, 1994) as well in modelling, identification and control of dynamic systems (Elliot et al, 2004;Frankel, 2003;Fraser, 2010;Kwon1 et al, 2006;Myers et al, 1992;Tsontzos et al, 2007;Wren et al, 2000).…”

Section: Introductionmentioning

confidence: 99%