A Deep Spatio-Temporal Model for EEG-Based Imagined Speech Recognition

Kumar, Pradeep; Scheme, Erik

doi:10.1109/icassp39728.2021.9413989

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…In the non-invasive techniques, the neural signals can also be sent back into the brain using transcranial magnetic stimulation (TMS) which has already been used by medics [ 49 ]. EEG signals are also used to recognize unspoken [ 60 ] and imagined speech from individuals [ 61 , 62 ]. Examples of non-invasive techniques are EEG [ 63 ], magneto-encephalography (MEG) [ 64 ], functional magnetic resonance imaging (fMRI) [ 65 ], and near-infrared spectroscopy (NIRS) [ 66 ].…”

Section: Methods Of Collecting Data From Brainmentioning

confidence: 99%

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Sen,

Sheehan,

Raman

et al. 2023

Sensors

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Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.

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Section: Methods Of Collecting Data From Brainmentioning

confidence: 99%

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Sen,

Sheehan,

Raman

et al. 2023

Sensors

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“…The entire EEG signal recorded for a particular class is of 10 seconds duration. As in [11], the EEG signals were divided into 250 ms duration (i.e. 32 samples) with a sliding increment of 64 ms (i.e.…”

Section: A Methodologymentioning

confidence: 99%

Analysis of EEG frequency bands for Envisioned Speech Recognition

Tripathi¹

2022

Preprint

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The use of Automatic speech recognition (ASR) interfaces have become increasingly popular in daily life for use in interaction and control of electronic devices. The interfaces currently being used are not feasible for a variety of users such as those suffering from a speech disorder, locked-in syndrome, paralysis or people with utmost privacy requirements. In such cases, an interface that can identify envisioned speech using electroencephalogram (EEG) signals can be of great benefit. Various works targeting this problem have been done in the past. However, there has been limited work in identifying the frequency bands (δ, θ, α, β, γ) of the EEG signal that contribute towards envisioned speech recognition. Therefore, in this work, we aim to analyze the significance of different EEG frequency bands and signals obtained from different lobes of the brain and their contribution towards recognizing envisioned speech. Signals obtained from different lobes and bandpass filtered for different frequency bands are fed to a spatio-temporal deep learning architecture with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The performance is evaluated on a publicly available dataset comprising of three classification tasks -digit, character and images. We obtain a classification accuracy of 85.93%, 87.27% and 87.51% for the three tasks respectively. The code for the implementation has been made available at https://github.com/ayushayt/ImaginedSpeechRecognition.

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“…Furthermore, deep learning approaches have recently taken a huge role for imagined speech recognition. Some of these techniques are Deep Neural Networks (DBN) (Lee and Sim, 2015 ; Chengaiyan et al, 2020 ), Correlation Networks (CorrNet) (Sharon and Murthy, 2020 ), Standardization-Refinement Domain Adaptation (SRDA) (Jiménez-Guarneros and Gómez-Gil, 2021 ), Extreme Learning Machine (ELM) (Pawar and Dhage, 2020 ), Convolutional Neural Networks (CNN) (Cooney et al, 2019 , 2020 ; Tamm et al, 2020 ), Recurrent Neural Networks (RNN) (Chengaiyan et al, 2020 ), and parallel CNN+RNN with and without autoencoders autoencoders (Saha and Fels, 2019 ; Saha et al, 2019a , b ; Kumar and Scheme, 2021 ).…”

Section: Classification Techniques In Literaturementioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

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Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

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A Deep Spatio-Temporal Model for EEG-Based Imagined Speech Recognition

Cited by 12 publications

References 13 publications

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Analysis of EEG frequency bands for Envisioned Speech Recognition

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

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