Vowel Classification of Imagined Speech in an Electroencephalogram using the Deep Belief Network

Lee, Tae‐Ju; Sim, Kwee-Bo

doi:10.5302/j.icros.2015.14.0073

Cited by 7 publications

(9 citation statements)

References 6 publications

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“…The findings from the study showed that the recognition rate of EEG MI data based on a DBN is better than that with the conventional SVM model. A novel technique of classification of imagined speech in EEG was proposed in Lee and Sim (2015), where the classification accuracy obtained was 87.96% with DBN. A P300-based Guilty Knowledge Test system was proposed in Kulasingham et al (2016).…”

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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“…The intra-subject training model CNNeeg1-1 with BD1 database (M = 0.6562, SD = 0.0123) ( Figure 7 ) and database BD2 (M = 0.8566, SD = 0. 0446) ( Figure 15 ) in EEG imagined vowel recognition (/a/,/e/,/i/,/o/,/u/) had an accuracy comparable or superior to other works developed with DL for imagined vowel recognition (/a/,/e/,/i/,/o/,/u/) such as: DBN with an accuracy of 80% with 6 subjects [ 18 , 40 ] and an accuracy of 87. 96% with 3 subjects [ 18 ]; with RNN an accuracy of 70% with 6 subjects [ 40 ]; with CNN an accuracy of 32.75% with 15 subjects [ 41 , 42 ] and an accuracy of 35.68% with 15 subjects [ 42 ].…”

Section: Discussionmentioning

confidence: 72%

“…Additionally, to reduce the effect of the low signal to noise ratio of EEG signals, there are alternative DL methods using EEG signal preprocessing for imagined vowels, such as: filtering from 2 Hz to 40 Hz, artifact detection and removal with Independent Component Analysis (ICA), and analysis with Hessian approximation preconditioning; eigenvalues of the covariance matrix [ 18 ]; 50 Hz LPF-IIR low-pass filters, 0.5 Hz HPF-IIR high-pass filters, and feature vectors consisting of EEG coherence, partial directed coherence (PDC), Direct Transfer Function (DFT) and transfer entropy [ 40 ].…”

Section: Related Workmentioning

confidence: 99%

“…In this sense, previous imagined speech works with conventional machine learning (ML) methods for imagined vowel recognition (/a/,/e/,/i/,/o/,/u/), have chosen to use time, frequency, or time-frequency transformations as the feature vector. Among the features that have been used for imagined vowel recognition (/a/,/e/,/i/,/o/,/u/) with ML are: statistical descriptors (average power, mean, variance, and standard deviation) [ 14 ]; common special patterns (CSPs) filtering, and adaptive collection (AC) [ 15 ]; Discrete Wavelet Transform (DWT) [ 16 , 17 ]; eigenvalues of the covariance matrix [ 18 ]; and mixed features such as descriptors (mean, variance, standard deviation, and skewness) and Sparse Regression Models [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods

Sarmiento

Villamizar

López

et al. 2021

Sensors

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The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.

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“…However, there are fewer studies using time domain analysis [12,13]. There are also vowel classification studies for the imagined speech [14].…”

Section: Introductionmentioning

confidence: 99%

Vowel Classification Based on Waveform Shapes

Tora¹,

Karacor²,

Uslu³

2019

Adv. sci. technol. eng. syst. j.

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Vowel classification is an essential part of speech recognition. In classical studies, this problem is mostly handled by using spectral domain features. In this study, a novel approach is proposed for vowel classification based on the visual features of speech waveforms. In sound vocalizing, the position of certain organs of the human vocal system such as tongue, lips and jaw is very effective on the waveform shapes of the produced sound. The motivation to employ visual features instead of classical frequency domain features is its potential usage in specific applications like language education. Even though this study is confined to Turkish vowels, the developed method can be applied to other languages as well since the shapes of the vowels show similar patterns. Turkish vowels are grouped into five categories. For each vowel group, a time domain speech waveform with an interval of two pitch periods is handled as an image. A series of morphological operations is performed on this speech waveform image to obtain the geometric characteristics representing the shape of each class. The extracted visual features are then fed into three different classifiers. The classification performances of these features are compared with classical methods. It is observed that the proposed visual features achieve promising classification rates.

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Vowel Classification of Imagined Speech in an Electroencephalogram using the Deep Belief Network

Cited by 7 publications

References 6 publications

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods

Vowel Classification Based on Waveform Shapes

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