Analysis of Speech Imagery using Functional and Effective EEG based Brain Connectivity Parameters

Chengaiyan, Sandhya; Anandhan., K

doi:10.4018/ijcini.2015100103

Cited by 12 publications

(1 citation statement)

References 28 publications

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“…It is different from effective connectivity which depends on an apriori model for the cases of causal mode of relationship. Functional connectivity using fMRI (Chengaiyan, S., & Anandhan, K., 2015) was studied on the regions of motor cortex of resting state human brain using the relevance of product moment correlation of BOLD time courses. Functional connectivity measures can be tested for its implication and the reliability of functional connectivity among the nodes infers the way in which the integration of nodes (Van Den Heuvel et al, 2010) is allied in the functional network.…”

Section: Introductionmentioning

confidence: 99%

Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI

Vidhusha

Anandan²

2016

International Journal of Cognitive Informatics and Natural Intelligence

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Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task i.e., “rest.” Adults with ASD have been found to show weaker connectivity relative to controls. This work focuses on analyzing the brain activation for autistic subjects, measured by fMRI during rest, relative to the control group using interhemispherical analysis. Though both groups activated similarly in cortical areas, indications of under connectivity were exhibited by the autistic group measured by Granger Causality and Conditional Granger Causality. Results show that as connectivity decreases, GC and CGC values also get decreased. The left hemisphere exhibits depreciation in the connectivity in comparison to that of right hemisphere for the autistic individuals whose GC and CGC values keeps decreasing in the left hemisphere seed regions. Finally, the results provide an approach for analyzing the cortical underconnectivity, in clinical relevance for diagnosing autism in children.

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Section: Introductionmentioning

confidence: 99%

Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI

Vidhusha

Anandan²

2016

International Journal of Cognitive Informatics and Natural Intelligence

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Effect of functional and effective brain connectivity in identifying vowels from articulation imagery procedures

Chengaiyan¹,

Kavitha²

2022

Cogn Process

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Identification of vowels in consonant–vowel–consonant words from speech imagery based EEG signals

2019

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Retrieval of unintelligible speech is a basic need for speech impaired and is under research for several decades. But retrieval of random words from thoughts needs a substantial and consistent approach. This work focuses on the preliminary steps of retrieving vowels from Electroencephalography (EEG) signals acquired while speaking and imagining of speaking a consonant-vowel-consonant (CVC) word. The process, referred to as Speech imagery is imagining of speaking to oneself silently in the mind. Speech imagery is a form of mental imagery. Brain connectivity estimators such as EEG coherence, Partial Directed Coherence, Directed Transfer Function and Transfer Entropy have been used to estimate the concurrency and causal dependence (direction and strength) between different brain regions. From brain connectivity results it has been observed that the left frontal and left temporal electrodes were activated for speech and speech imagery processes. These brain connectivity estimators have been used for training Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN) for identifying the vowel from the subject's thought. Though the accuracy level was found to be varying for each vowel while speaking and imagining of speaking the CVC word, the overall classification accuracy was found to be 72% while using RNN whereas a classification accuracy of 80% was observed while using DBN. DBN was found to outperform RNN in both the speech and speech imagery processes. Thus, the combination of brain connectivity estimators and deep learning techniques appear to be effective in identifying the vowel from EEG signals of subjects' thought.

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Analysis of Speech Imagery using Functional and Effective EEG based Brain Connectivity Parameters

Cited by 12 publications

References 28 publications

Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI

Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI

Effect of functional and effective brain connectivity in identifying vowels from articulation imagery procedures

Identification of vowels in consonant–vowel–consonant words from speech imagery based EEG signals

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