Comparison of Feature-Model Variants for coSpeech-EEG Classification

Sharon, Rini A.; Murthy, Hema A.

doi:10.1109/ncc48643.2020.9056076

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Despite the numerous applications of decoding brain signals to establish communication, a majority of existing BCI modules are based on non-speech inputs like mental counting [8,9], imagined motor movements [10,11] and visual/auditory evoked potentials [12,13]. A more naturalistic design would involve the subject using speech based inputs as controls to the BCI devices [14,15]. There exist different speech-associated cognitive phases that can be chosen as controls.…”

Section: Introductionmentioning

confidence: 99%

Correlation based Multi-phasal models for improved imagined speech EEG recognition

Sharon¹,

Murthy²

2020

Workshop on Speech, Music and Mind (SMM 2020)

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Translation of imagined speech electroencephalogram(EEG) into human understandable commands greatly facilitates the design of naturalistic brain computer interfaces. To achieve improved imagined speech unit classification, this work aims to profit from the parallel information contained in multi-phasal EEG data recorded while speaking, imagining and performing articulatory movements corresponding to specific speech units. A bi-phase common representation learning module using neural networks is designed to model the correlation and reproducibility between an analysis phase and a support phase. The trained Correlation Network is then employed to extract discriminative features of the analysis phase. These features are further classified into five binary phonological categories using machine learning models such as Gaussian mixture based hidden Markov model and deep neural networks. The proposed approach further handles the non-availability of multi-phasal data during decoding. Topographic visualizations along with result-based inferences suggest that the multi-phasal correlation modelling approach proposed in the paper enhances imaginedspeech EEG recognition performance.

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Section: Introductionmentioning

confidence: 99%

Correlation based Multi-phasal models for improved imagined speech EEG recognition

Sharon¹,

Murthy²

2020

Workshop on Speech, Music and Mind (SMM 2020)

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“…While speech-EEG based BCI systems commonly focus on speech-unit classification(where vowels, syllables, words and phrases are considered as units [7,2,8,9]), the speech-silence portions associated with these units lack analysis. Since speech and EEG are both well correlated, temporally informative signals, studying the inherent existence of silence signatures in the brain is both meaningful and interesting [10,11].…”

Section: Introductionmentioning

confidence: 99%

The "Sound of Silence" in EEG -- Cognitive voice activity detection

Sharon¹,

Murthy²

2020

Preprint

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Speech cognition bears potential application as a brain computer interface that can improve the quality of life for the otherwise communication impaired people. While speech and resting state EEG are popularly studied, here we attempt to explore a "non-speech"(NS) state of brain activity corresponding to the silence regions of speech audio. Firstly, speech perception is studied to inspect the existence of such a state, followed by its identification in speech imagination. Analogous to how voice activity detection is employed to enhance the performance of speech recognition, the EEG state activity detection protocol implemented here is applied to boost the confidence of imagined speech EEG decoding. Classification of speech and NS state is done using two datasets collected from laboratory-based and commercial-based devices. The state sequential information thus obtained is further utilized to reduce the search space of imagined EEG unit recognition. Temporal signal structures and topographic maps of NS states are visualized across subjects and sessions. The recognition performance and the visual distinction observed demonstrates the existence of silence signatures in EEG.

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Neural Speech Decoding During Audition, Imagination and Production

et al. 2020

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Interpretation of neural signals to a form that is as intelligible as speech facilitates the development of communication mediums for the otherwise speech/motor-impaired individuals. Speech perception, production, and imagination often constitute phases of human communication. The primary goal of this paper is to analyze the similarity between these three phases by studying electroencephalogram(EEG) patterns across these modalities, in order to establish their usefulness for brain computer interfaces. Neural decoding of speech using such non-invasive techniques necessitates the optimal choice of signal analysis and translation protocols. By employing selection-by-exclusion based temporal modeling algorithms, we discover fundamental syllable-like units that reveal similar set of signal signatures across all the three phases. Significantly higher than chance accuracies are recorded for single trial multi-unit EEG classification using machine learning approaches over three datasets across 30 subjects. Repeatability and subject independence tests performed at every step of the analysis further strengthens the findings and holds promise for translating brain signals to speech non-invasively.

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Comparison of Feature-Model Variants for coSpeech-EEG Classification

Cited by 4 publications

References 29 publications

Correlation based Multi-phasal models for improved imagined speech EEG recognition

Correlation based Multi-phasal models for improved imagined speech EEG recognition

The "Sound of Silence" in EEG -- Cognitive voice activity detection

Neural Speech Decoding During Audition, Imagination and Production

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