Voice command recognition using EEG signals

Rosinova, Marianna; Lojka, Martin; Staš, Ján; Juhar, Jozef

doi:10.23919/elmar.2017.8124457

Cited by 17 publications

(10 citation statements)

References 5 publications

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“…1) Large-set Decoding: Majority of works classify a closed-set vocabulary of units such as words [57], [58] and phrasal blocks [30]. This makes the scalability of the protocol to newer unseen test instances difficult.…”

Section: E Methodological Design Advantagesmentioning

confidence: 99%

“…Most of the works in the literature are based on a two-class problem [28], [29]. When a multi-class framework is considered, a significant decrease in performance is observed [30], [31]. The work proposed in this paper not only addresses the multi-class problem but also considers continuous speech.…”

Section: B Related Work : Co-speech Neural Signalsmentioning

confidence: 99%

“…• Previous works have attempted imagined isolated speech-unit classification [28], [29], [31], spoken speech classification [30] and heard speech envelope correlation analysis [19], [20]. This work attempts to compare speech-unit classification across all three different phases of co-speech EEG, namely, imagination, perception, and production.…”

Section: Contributionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: E Methodological Design Advantagesmentioning

confidence: 99%

Section: B Related Work : Co-speech Neural Signalsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

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Neural Speech Decoding During Audition, Imagination and Production

et al. 2020

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“…A closely related work in [20] intends to classify 50 phrases of speech EEG signals with a classification accuracy of 5% . However, the proposed method differs from the above in the band-based feature extraction and classification module and is shown to outperform the same with respect to accuracy and robustness.…”

Section: Motivation and Related Workmentioning

confidence: 99%

An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG

Sharon

Narayanan

Sur

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Clinical applicability of electroencephalography (EEG) is well established, however the use of EEG as a choice for constructing brain computer interfaces to develop communication platforms is relatively recent. To provide more natural means of communication, there is an increasing focus on bringing together speech and EEG signal processing. Quantifying the way our brain processes speech is one way of approaching the problem of speech recognition using brain waves. This paper analyses the feasibility of recognizing syllable level units by studying the temporal structure of speech reflected in the EEG signals. The slowly varying component of the delta band EEG(0.3-3Hz) is present in all other EEG frequency bands. Analysis shows that removing the delta trend in EEG signals results in signals that reveals syllable like structure. Using a 25 syllable framework, classification of EEG data obtained from 13 subjects yields promising results, underscoring the potential of revealing speech related temporal structure in EEG.

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“…At the same time, current technologies still suffer from major limitations that require innovative new solutions to overcome. Brain-computer interface (BCI) devices that utilize neural signals from invasive implants remain inaccessible for the majority of their target user population [40], whereas BCI devices that decode from electroencephalography (EEG) signals remain insufficiently fast with low accuracy and a limited range of classification abilities [32,35,36]. Ultimately, the most realistic chance for wide-spread application of speech decoding technology will require a non-invasive method that can achieve a similar classification accuracy accomplished by invasive methods.…”

Section: Introductionmentioning

confidence: 99%

Exploratory methods for high-performance EEG speech decoding

Comstock

Lainscsek

Carvalho

et al. 2021

Preprint

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State-of-the-art technologies in neural speech decoding utilize data collected from microwires or microarrays implanted directly into the cerebral cortex. Yet as a tool accessible only to individuals with implanted electrodes, speech decoding from devices of this nature is severely limited in its implementation, and cannot be considered a viable solution for widespread application. Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%), but only from very small classification tasks, with more complex tasks typically yielding a limited (20-50%) classification accuracy. We propose a novel combination of technologies in which transcranial magnetic stimulation (TMS) is first applied to augment the neural signals of interest, producing a greater signal-to-noise ratio in the EEG data. Next, delay differential analysis (DDA) – a cutting-edge computational method based on nonlinear dynamics – is implemented to capture the widest range of information available in the neural signal, by incorporating both linear and nonlinear dynamics.

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Voice command recognition using EEG signals

Cited by 17 publications

References 5 publications

Neural Speech Decoding During Audition, Imagination and Production

Neural Speech Decoding During Audition, Imagination and Production

An Empirical Study of Speech Processing in the Brain by Analyzing the Temporal Syllable Structure in Speech-input Induced EEG

Exploratory methods for high-performance EEG speech decoding

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