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.
Speech imagery is one form of mental imagery which refers to the imagining of speaking a word to oneself silently in the mind without any articulation movement. In this work, electroencephalography (EEG) signals were acquired while speaking and during the imagining of speaking consonant-vowel-consonant (CVC) words in multiple trials of different time frames. Relative powers were computed for each EEG frequency band. It has been observed that relative power of alpha and theta bands was dominant. Phase Locking Value (PLV), a functional brain connectivity parameter has been estimated to understand the phase synchronicity between two brain regions. PLV results show that the left hemispheric frontal and temporal electrodes has maximum phase lock in alpha and theta band during speech and speech imagery process. The combination of brain connectivity estimators and signal processing techniques will thus be a reliable framework for understanding the nature of speech imagery signals captured through EEG.
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