G
G R Kiran Kumar
Researcher at Indian Institute of Technology Madras
Publications - 9
Citations - 49
G R Kiran Kumar is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Filter bank & Benchmark (computing). The author has an hindex of 3, co-authored 9 publications receiving 30 citations.
Papers
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Journal ArticleDOI
Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs
TL;DR: A novel subject-specific target detection framework, sum of squared correlations (SSCOR), for improving the performance in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs).
Journal ArticleDOI
Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.
TL;DR: periodic component analysis ( π CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise and provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost.
Journal ArticleDOI
Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain-computer interfaces.
TL;DR: A significant improvement in the target identification performance demonstrates that the proposed LCSE method can be seen as a promising potential candidate for efficient SSVEP detection in brain-computer interface (BCI) systems.
Journal ArticleDOI
Constructing an exactly periodic subspace for enhancing SSVEP based BCI
TL;DR: The results reveal that the proposed EPSD spatial filter significantly enhances the performance of target detection and further statistical tests confirm that the EPSD is a potential alternative to the existing SSVEP spatial filters for realizing an efficient BCI system.
Proceedings ArticleDOI
Filter bank extensions for subject non-specific SSVEP based BCIs
TL;DR: The results demonstrate that the proposed two stage (a filter bank stage followed by SSVEP detection) implementation of popular multichannel algorithms provide significant improvement in performance at short datalengths of < 2.75 s and can be viewed as a potential standard detection approach across allSSVEP identification problems.