R
Rami Saab
Researcher at University of Toronto
Publications - 12
Citations - 429
Rami Saab is an academic researcher from University of Toronto. The author has contributed to research in topics: Brain–computer interface & Attentional control. The author has an hindex of 6, co-authored 12 publications receiving 304 citations. Previous affiliations of Rami Saab include East China University of Science and Technology & Tsinghua University.
Papers
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Journal ArticleDOI
Towards correlation-based time window selection method for motor imagery BCIs.
TL;DR: The results demonstrate that the proposed CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches, and suggest that it holds promise as a general feature extraction approach for MI-based BCIs.
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A Hybrid Brain–Computer Interface Based on the Fusion of P300 and SSVEP Scores
TL;DR: The experimental results indicated that the 4-D hybrid paradigm outperformed the DRC paradigm and that the MPE fusion achieved higher accuracy compared with the other approaches, suggesting that the proposed hybrid BCI system could be used in the design of a high-performance BCI-based keyboard.
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An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface.
TL;DR: A direction-congruent bimodal paradigm by randomly and simultaneously presenting auditory and tactile stimuli from the same direction is designed that holds promise as a practical visual saccade-independent P300 BCI.
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A Dynamic Window Recognition Algorithm for SSVEP-Based Brain–Computer Interfaces Using a Spatio-Temporal Equalizer
TL;DR: It is suggested that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.
Journal ArticleDOI
Novel hybrid brain–computer interface system based on motor imagery and P300
Cili Zuo,Jing Jin,Erwei Yin,Rami Saab,Yangyang Miao,Xingyu Wang,Dewen Hu,Andrzej Cichocki,Andrzej Cichocki,Andrzej Cichocki +9 more
TL;DR: A novel hybrid BCI paradigm based on MI and P300 is proposed, in which unreliable P300 classifications are corrected by reliable MI classifications, and the training data size can be reduced through fusion of these two modalities.