An efficient P300-speller for Arabic letters

Kabbara, Aya; Hassan, Mahmoud; Khalil, Mohamad; Eid, Hassan; El-Falou, Wassim

doi:10.1109/icabme.2015.7323272

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…In Kabbara et al [ 69 ], a P300 Matrix Speller was presented in Arabic letters for the first time. A 6 × 5 matrix displays all the Arabic letters in an RCP, where the intensification was the random flashing of rows and columns.…”

Section: Review Of Bci Spellersmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

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A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

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Section: Review Of Bci Spellersmentioning

confidence: 99%

Brain–Computer Interface Spellers: A Review

et al. 2018

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“…Although most of the P300 speller paradigms are developed for English language, P300 BCI with other alphabets, such as alphabets in Chinese (Jin et al, 2010;Minett et al, 2012) , Japanese (Yamamoto, Yoshikawa, & Furuhashi, 2015) and Arabic languages (Kabbara, Hassan, Khalil, Eid, & El-Falou, 2015) have also been published. A practical BCI system should be able to produce accurate results with high ITR.…”

Section: Downloaded By [University Of California Santa Barbara] At 19mentioning

confidence: 99%

Binary DE-Based Channel Selection and Weighted Ensemble of SVM Classification for Novel Brain–Computer Interface Using Devanagari Script-Based P300 Speller Paradigm

Chaurasiya

Londhe

Ghosh

2016

International Journal of Human–Computer Interaction

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“…The functional connectivity matrices representing the estimates of interaction between all pairs of EEG channels were computed in the different frequency bands (θ(4-8 Hz), α1(8-10 Hz), α2(10-13 Hz), ß(13-30 Hz), γ(30-45 Hz), and broad band (0.2-45 Hz)). Here, the connectivity was assessed using the phase synchronization (PS) method as it is less susceptible to the effects of artifacts [13] and was successfully used in several scalp-level EEG connectivity analyses [14], [15] such as Brain Computer Interfaces . In particular, we used the phase locking value (PLV) proposed in [16] and defined as:…”

Section: Functional Connectivity Analysismentioning

confidence: 99%

A scalp-EEG network-based analysis of Alzheimer's disease patients at rest

Kabbara

Falou

Khalil

et al. 2017

2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)

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Abstract-Most brain disorders including Alzheimer's disease (AD) are related to alterations in the normal brain network organization and function. Exploring these network alterations using non-invasive and easy to use technique is a topic of great interest. In this paper, we collected EEG resting-state data from AD patients and healthy control subjects. Functional connectivity between scalp EEG signals was quantified using the phase locking value (PLV) for 6 frequency bands, θ (4-8 Hz), α1(8-10 Hz), α2(10-13 Hz), ß(13-30 Hz), γ(30-45 Hz), and broad band (0.2-45 Hz). To assess the differences in network properties, graph-theoretical analysis was performed. AD patients showed decrease of mean connectivity, average clustering and global efficiency in the lower alpha band. Positive correlation between the cognitive score and the extracted graph measures was obtained, suggesting that EEG could be a promising technique to derive new biomarkers of AD diagnosis.

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An efficient P300-speller for Arabic letters

Cited by 6 publications

References 16 publications

Brain–Computer Interface Spellers: A Review

Brain–Computer Interface Spellers: A Review

Binary DE-Based Channel Selection and Weighted Ensemble of SVM Classification for Novel Brain–Computer Interface Using Devanagari Script-Based P300 Speller Paradigm

A scalp-EEG network-based analysis of Alzheimer's disease patients at rest

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