EEG gamma-band activity during audiovisual speech comprehension in different noise environments

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

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.

Neural Oscillations Orchestrate Multisensory Processing

At any given moment, we receive input through our different sensory systems, and this information needs to be processed and integrated. Multisensory processing requires the coordinated activity of distinct cortical areas. Key mechanisms implicated in these processes include local neural oscillations and functional connectivity between distant cortical areas. Evidence is now emerging that neural oscillations in distinct frequency bands reflect different mechanisms of multisensory processing. Moreover, studies suggest that aberrant neural oscillations contribute to multisensory processing deficits in clinical populations, such as schizophrenia. In this article, we review recent literature on the neural mechanisms underlying multisensory processing, focusing on neural oscillations. We derive a framework that summarizes findings on (1) stimulus-driven multisensory processing, (2) the influence of top-down information on multisensory processing, and (3) the role of predictions for the formation of multisensory perception. We propose that different frequency band oscillations subserve complementary mechanisms of multisensory processing. These processes can act in parallel and are essential for multisensory processing.