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

Lin, Yanfei; Liu, Baolin; Liu, Zhiwen; Gao, Xiaorong

doi:10.1007/s11571-015-9333-5

Cited by 8 publications

(3 citation statements)

References 53 publications

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“…The authors found suppression of central beta-band power, which was modulated by concurrent noise stimulation. A further study, which examined noise during audiovisual speech perception, has also highlighted the role of early gamma-band oscillations (Lin and others 2015). The study revealed that auditory noise increases early induced and evoked gamma power, as well as ITC at frontal electrodes.…”

Section: Audiovisual Speech Processingmentioning

confidence: 99%

Neural Oscillations Orchestrate Multisensory Processing

2018

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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.

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Section: Audiovisual Speech Processingmentioning

confidence: 99%

Neural Oscillations Orchestrate Multisensory Processing

2018

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“…EEG measures the mental activity of the brain directly from the scalp while performing a given cognitive task. The neuronal oscillations related to speech perception and speech comprehension have been identified as theta and gamma frequency bands (Ghitza and Greenberg 2009;Ghitza 2013;Lin et al 2015). Brain regions associated with speech and imagined speech are identified as homunculus, Broca's area and Wernicke's area (Hickok and Poeppel 2000;Poeppel and Hickok 2007).…”

Section: Introductionmentioning

confidence: 99%

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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“…Therefore, at the high SNR auditory information was sufficient for human brain to identify speech, while visual information was less utilized, which made the audio-visual speech recognition rate be similar to that of audio-only. On the other hand, the auditory information was weak at the low SNR, and the visual information was relatively strong, which made the audio-visual speech recognition rate be similar to that of the visual-only condition [25,26] .…”

Section: Comparison Of Speech Recognition Ability Between Different Modalitiesmentioning

confidence: 99%

Sensitivity of N400 effect during speech comprehension under the uni- and bi-modality conditions

Lin

Liu

Gao

2022

Tsinghua Sci. Technol.

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N400 is an objective electrophysiological index in semantic processing for brain. This study focuses on the sensitivity of N400 effect during speech comprehension under the uni-and bi-modality conditions. Varying the Signal-to-Noise Ratio (SNR) of speech signal under the conditions of Audio-only (A), Visual-only (V, i.e., lip-reading), and Audio-Visual (AV), the semantic priming paradigm is used to evoke N400 effect and measure the speech recognition rate. For the conditions A and high SNR AV, the N400 amplitudes in the central region are larger; for the conditions of V and low SNR AV, the N400 amplitudes in the left-frontal region are larger. The N400 amplitudes of frontal and central regions under the conditions of A, AV, and V are consistent with speech recognition rate of behavioral results. These results indicate that audio-cognition is better than visual-cognition at high SNR, and visual-cognition is better than audio-cognition at low SNR.

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

Cited by 8 publications

References 53 publications

Neural Oscillations Orchestrate Multisensory Processing

Neural Oscillations Orchestrate Multisensory Processing

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

Sensitivity of N400 effect during speech comprehension under the uni- and bi-modality conditions

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