Discriminating Between Imagined Speech and Non-Speech Tasks Using EEG

Al-Saleh, Mashael; Moore, Roger K.; Christensen, Heidi; Arvaneh, Mahnaz

doi:10.1109/embc.2018.8512681

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…This process can be carried on the time domain, frequency domain, and spatial domain. In the time domain, the feature extraction process is often done through statistical analysis, obtaining statistical features such as standard deviation (SD), root mean square (RMS), mean, variance, sum, maximum, minimum, Hjorth parameters, sample entropy, autoregressive (AR) coefficients, among others (Riaz et al, 2014 ; Iqbal et al, 2016 ; AlSaleh et al, 2018 ; Cooney et al, 2018 ; Paul et al, 2018 ; Lee et al, 2019 ). On the other hand, the most common methods used to extract features from the frequency domain include Mel Frequency Cepstral Coefficients (MFCC), Short-Time Fourier transform (STFT), Fast Fourier Transform (FFT), Wavelet Transform (WT), Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT) (Riaz et al, 2014 ; Salinas, 2017 ; Cooney et al, 2018 ; Garćıa-Salinas et al, 2018 ; Panachakel et al, 2019 ; Pan et al, 2021 ).…”

Section: Feature Extraction 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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Section: Feature Extraction 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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“…In this work, we focus on non-invasive brain signals acquired using EEG technique. Despite of the low spatial resolution of EEG, it has been widely popular owing to it's simplistic nature and little to no discomfort to the user [4].…”

Section: Introductionmentioning

confidence: 99%

“…The recognition of imagined speech using EEG has been attempted at various levels such as word, syllable, vowel imagination [4]- [6]. In their work, Gonzalez-Castaneda et al [7] proposed an imagined word classification system using EEG comprising of 5 words.…”

Section: Introductionmentioning

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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“…Many BCI researchers have proposed using electroencephalogram (EEG) signals as a preferred non-invasive method of inferring brain activity. Although they offer less depth and spatial resolution than other imaging methods, EEG systems are now portable and affordable and offer better temporal resolution than alternative approaches [4]. EEGbased studies of imagined speech recognition are emerging, broadly divided into word, syllable, and vowel imagination, however, they remain limited in the number and variety of imagined classes [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Although they offer less depth and spatial resolution than other imaging methods, EEG systems are now portable and affordable and offer better temporal resolution than alternative approaches [4]. EEGbased studies of imagined speech recognition are emerging, broadly divided into word, syllable, and vowel imagination, however, they remain limited in the number and variety of imagined classes [4,5,6]. For example, Gonzalez-Castaneda et al [7] proposed a five imagined word classification system using EEG.…”

Section: Introductionmentioning

confidence: 99%

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

Kumar

Scheme

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Automatic speech recognition interfaces are becoming increasingly pervasive in daily life as a means of interacting with and controlling electronic devices. Current speech interfaces, however, are infeasible for a variety of users and use cases, such as patients who suffer from locked-in syndrome or those who need privacy. In these cases, an interface that works based on envisioned speech, the idea of imagining what one wants to say, could be of benefit. Consequently, in this work, we propose an imagined speech Brain-Computer-Interface (BCI) using Electroencephalogram (EEG) signals. EEG signals are processed using a deep spatio-temporal learning architecture with 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), respectively. LSTM units are implemented in a many-to-many fashion to produce a time series of imagined speech outputs. Using this series, the performance of the system is boosted using majority vote (MV) post-processing to further improve results. The performance is evaluated on two publicly available datasets; one to test the performance of the tuned model, and another to test its generalization to a new dataset. The proposed architecture outperforms previous results with improvements of up to 23.7%.

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Discriminating Between Imagined Speech and Non-Speech Tasks Using EEG

Abstract: This is a repository copy of Discriminating between imagined speech and non-speech tasks using EEG.

Cited by 7 publications

References 16 publications

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

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

Analysis of EEG frequency bands for Envisioned Speech Recognition

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

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