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Phoneme-based Imagined Vowel Identification from Electroencephalographic Sub-Band Oscillations during Speech Imagery Procedures

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Title Phoneme-based Imagined Vowel Identification from Electroencephalographic Sub-Band Oscillations during Speech Imagery Procedures
 
Creator Retnapandian, Anandha Sree
Anandan, Kavitha
 
Subject Electroencephalography
Imagined vowel identification
Phoneme
Recurrent neural network
Speech imagery
 
Description 756-766
Speech Imagery (SI) corresponds to imagining speaking an intended speech or a segment of speech. Decoding the SI
process aids in building speech-based neural prosthetic devices. Though SI-based research has been carried out to decode
imagined speech for more than a decade, there is a lag in achieving the naturalness of the spoken language. This is because
the words are built as the combination of phonemes in any natural language, but the research so far has been involving the
SI of vowels only. Hence, this work focuses on identifying the vowels from EEG signals acquired while imagining the
corresponding phonemes. The acquisition process was repeated for multiple trials. The EEG signals were decomposed into
five sub-band frequencies to analyze the activity during SI tasks. The energy coefficients extracted from the sub-band
frequencies were employed in training the Recurrent Neural Network to classify the English vowels. Further, to emphasize
the importance of training the classifier with multi-trial data, the results were compared with that of the single-trial data
acquired from the same set of participants, and an accuracy of 84.5% and 88.9% were achieved for single and multi-trial
protocols, respectively. The analysis using multi-trial data was able to achieve 4.4% higher accuracy when compared to
single-trial data. Higher activations in the theta band during the speech imagery tasks and higher Classification accuracy
while applying theta band features show the capability of using the theta band features in imagined speech decoding tasks.
 
Date 2023-07-06T04:47:48Z
2023-07-06T04:47:48Z
2023-07
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62269
https://doi.org/10.56042/jsir.v82i07.2899
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(07) [July 2023]