Tensor Decomposition for Imagined Speech Discrimination in EEG

García-Salinas, Jesús S.; Villaseñor-Pineda, Luis; Reyes-García, Carlos A.; Torres-García, Alejandro A.

doi:10.1007/978-3-030-04497-8_20

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…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 ). Additionally, there is a method called Bag-of-Features (BoF) proposed by Lin et al ( 2012 ), in which a time-frequency analysis is done to convert the signal into words using Sumbolic Arregate approXimation (SAX).…”

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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“…[24] classi ed vowels using random forest a er down sampling the data and reported an accuracy of 22.32%. Discrimination of imagined speech in EEG was proposed in [45] using Tensor decomposition. Hilbert transform and Hilbert spectrum methods were used by [46], [47], and [48] to decode imagined speech using EEG signal, both studies used two different syllables during the experiment but with four and seven subjects respectively.…”

Section: Decoding Of Imagined Speech Based On Eegmentioning

confidence: 99%

“…Hilbert transform and Hilbert spectrum methods were used by [46], [47], and [48] to decode imagined speech using EEG signal, both studies used two different syllables during the experiment but with four and seven subjects respectively. Wavelet transform was used for feature extraction with alternated least squares approximation and down sample the data for vowel classi cation, they obtained the accuracy of 59.70% using SVM classi er [45]. Multi-class classi cation of words was proposed in [49] using connectivity features.…”

Section: Decoding Of Imagined Speech Based On Eegmentioning

confidence: 99%

Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition

Saminu

Zhang

et al. 2021

J. Appl. Mat. Tech.

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The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).

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“…The dataset authors themselves [19] provided the initial classification, where they down sampled the data and used a RF classifier to get an accuracy of 22.32% for vowels. Garcia-Salinas et al [20] also down sampled the data and used wavelet transform with alternated least squares approximation with a linear SVM classifier and got an average inter-subject accuracy of 59.70% for words on the first three subjects. Cooney et al [21] used a deep and a shallow CNN to classify word-pairs from the dataset and used independent component analysis with Hessian approximation to achieve an average accuracy of 62.37% and 60.88% for the deep and shallow CNNs, respectively.…”

Section: Recent Approachesmentioning

confidence: 99%

Classification of Vowels from Imagined Speech with Convolutional Neural Networks

Tamm

Muhammad

2020

Computers

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Imagined speech is a relatively new electroencephalography (EEG) neuro-paradigm, which has seen little use in Brain-Computer Interface (BCI) applications. Imagined speech can be used to allow physically impaired patients to communicate and to use smart devices by imagining desired commands and then detecting and executing those commands in a smart device. The goal of this research is to verify previous classification attempts made and then design a new, more efficient neural network that is noticeably less complex (fewer number of layers) that still achieves a comparable classification accuracy. The classifiers are designed to distinguish between EEG signal patterns corresponding to imagined speech of different vowels and words. This research uses a dataset that consists of 15 subjects imagining saying the five main vowels (a, e, i, o, u) and six different words. Two previous studies on imagined speech classifications are verified as those studies used the same dataset used here. The replicated results are compared. The main goal of this study is to take the proposed convolutional neural network (CNN) model from one of the replicated studies and make it much more simpler and less complex, while attempting to retain a similar accuracy. The pre-processing of data is described and a new CNN classifier with three different transfer learning methods is described and used to classify EEG signals. Classification accuracy is used as the performance metric. The new proposed CNN, which uses half as many layers and less complex pre-processing methods, achieved a considerably lower accuracy, but still managed to outperform the initial model proposed by the authors of the dataset by a considerable margin. It is recommended that further studies investigating classifying imagined speech should use more data and more powerful machine learning techniques. Transfer learning proved beneficial and should be used to improve the effectiveness of neural networks.

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Tensor Decomposition for Imagined Speech Discrimination in EEG

Cited by 7 publications

References 22 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

Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition

Classification of Vowels from Imagined Speech with Convolutional Neural Networks

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