Multiclass covert speech classification using extreme learning machine

Pawar, Dipti; Dhage, Sudhir

doi:10.1007/s13534-020-00152-x

Cited by 41 publications

(35 citation statements)

References 37 publications

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“…These words were chosen because the words correspond to instructions a user might use for controlling a computer cursor or a wheelchair. In a very recent work by Pawar and Dhage ( 2020 ), a similar set of prompts was used. Pawar and Dhage used the prompts “left,” “right,” “up,” and “down.” This choice of prompts is not only motivated by the usefulness of these prompts in practical applications but also because of their diverse manner and places of articulation.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In addition to this band, frequency band from 2 to 50 Hz is also used in several works. In all the articles reviewed in this work, only seven works use the frequency band above 80 Hz and out of these seven, only five works (Jahangiri et al, 2018 , 2019 ; Koizumi et al, 2018 ; Jahangiri and Sepulveda, 2019 ; Pawar and Dhage, 2020 ) use frequency band from 80 to 100 Hz.…”

Section: Preprocessingmentioning

confidence: 99%

“…Jahangiri et al have used the entire frequency range up to 128 Hz except for the narrow band from 49.2 to 50.8 Hz to remove the line noise whereas Koizumi et al ( 2018 ) have used the frequency range from 1 to 120 Hz and have reported a higher classification accuracy when features extracted from the high gamma band (60–120 Hz) are used. Pawar and Dhage ( 2020 ) have used the frequency range from 0.5 to 128 Hz. Jahangiri et al have supported the use of this band based on the high gamma activity observed in electrocorticography (ECoG) data recorded during imagined speech tasks (Greenlee et al, 2011 ; Llorens et al, 2011 ) and have reported higher activity in the band 70 to 128 Hz during imagination of the prompts.…”

Section: Preprocessingmentioning

confidence: 99%

“…Section 4.4 compares the performance of the systems proposed by Koizumi et al ( 2018 ) and Pawar and Dhage ( 2020 ) based on Cohen's kappa value.…”

Section: Preprocessingmentioning

confidence: 99%

“…García et al ( 2012 ), Sereshkeh et al ( 2017a ), Jahangiri et al ( 2019 , 2018 ), Jahangiri and Sepulveda ( 2019 ), Pawar and Dhage ( 2020 ), and Panachakel et al ( 2020a ) used wavelet transform for extracting features. Specifically, Garcia et al used Daubechies 2 (db2) wavelets, Jahangiri et al have used Gabor wavelets and Sereshkeh et al, Pawar and Dhage and Panachakel et al used db4 wavelets as the mother wavelets.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

See 4 more Smart Citations

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

“…Section 4.4 compares the performance of the systems proposed by Koizumi et al ( 2018 ) and Pawar and Dhage ( 2020 ) based on Cohen's kappa value.…”

Section: Preprocessingmentioning

confidence: 99%

Section: Feature Extraction and Classificationmentioning

confidence: 99%

See 3 more Smart Citations

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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A Review of Research on Brain-Computer Interface Based on Imagined Speech

Wang

Ding

Shan

et al. 2021

Communications in Computer and Information Science

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Recommendations for Responsible Development and Application of Neurotechnologies

et al. 2021

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Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including “Neurorights,” for data privacy, security, and consent to empower neurotechnology users’ control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.

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Multiclass covert speech classification using extreme learning machine

Cited by 41 publications

References 37 publications

Decoding Covert Speech From EEG-A Comprehensive Review

Decoding Covert Speech From EEG-A Comprehensive Review

A Review of Research on Brain-Computer Interface Based on Imagined Speech

Recommendations for Responsible Development and Application of Neurotechnologies

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