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Multiview MAX-VAR canonical correlation approach for enhancing ssvep based bcis
G. R. Kiran Kumar,
Published in Institute of Electrical and Electronics Engineers Inc.
2019
Abstract
This study proposes and validates a novel steady-state visual evoked potential (SSVEP) detection approach, multiview MAX-VAR canonical correlation, that finds a common unique subspace that encompasses all the SSVEP responses pertaining to a specific subject. The method employs a generalized canonical correlation framework that efficiently computes a projection matrix that optimizes test data to achieve higher SSVEP identification performance. We used a SSVEP benchmark dataset using a 40 target BCI experiment to evaluate the proposed method. The results demonstrate that the multiview MAX-VAR canonical correlation approach outperforms the compared methods with respect to both accuracy and information transfer rates (ITRs). From the statistical significance tests, it is observed that the proposed approach effectively achieves superior performance at short window lengths making it a propitious algorithm for real time brain computer interfaces (BCI). © 2019 IEEE.
Concepts (12)
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    Interface states
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    MEDICAL INFORMATICS
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    Value engineering
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    Benchmark datasets
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    CANONICAL CORRELATIONS
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    COMMON UNIQUE SUBSPACE
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    DETECTION APPROACH
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    INFORMATION TRANSFER RATE
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    PROJECTION MATRIX
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    STATISTICAL SIGNIFICANCE TEST
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    STEADY STATE VISUAL EVOKED POTENTIALS
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    BRAIN COMPUTER INTERFACE