Record Details


Field	Value

Title	Transforming Imagined Thoughts into Speech Using a Covariance-Based Subset Selection Method

Creator	Agarwal, Prabhakar Kumar, Sandeep

Subject	Brain-computer interfacing Covariance Electroencephalogram

Description	180-183 With the advancement of engineering solutions in the medical domain, the patient’s life can become comfortable. This work recognizes the silent speech of three words. The decoding of silent speech can be useful for patients who are in a locked-in syndrome state. Moreover, it is also applicable to entertainment, cognitive biometrics, and brain-computer interfacing. Brain waves of these imagined words in the delta, theta, alpha, beta, gamma, and high gamma frequency bands are analysed. Covariance based connectivity features are extracted in each frequency band. The principal features which represent more than 95% of the variance are selected as a subset of the covariance connectivity matrix. This sub-set is tested on five classifiers. The maximum accuracy achieved is 76.4% in the theta band. Also, theta and high gamma band contain maximum information about imagined speech with average accuracies of 68.32% and 62.09% respectively.

Date	2021-03-16T07:12:58Z 2021-03-16T07:12:58Z 2021-03

Type	Article

Identifier	0975-0959 (Online); 0301-1208 (Print) http://nopr.niscair.res.in/handle/123456789/56517

Language	en_US

Rights	CC Attribution-Noncommercial-No Derivative Works 2.5 India

Publisher	NISCAIR-CSIR, India

Source	IJPAP Vol.59(03) [March 2021]

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