Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning

Mahapatra, Nrushingh Charan; Bhuyan, Prachet

doi:10.1155/2022/1374880

Cited by 4 publications

(2 citation statements)

References 35 publications

(35 reference statements)

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“…The classification of vowels has already been done in previous years too. Majority of researches done so far make use of multi-channel EEG devices (Table 1) [16] [17] [18] [19] [20].…”

Section: Methodsmentioning

confidence: 99%

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Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Iqbal,

Bhardwaj,

Suhag

et al. 2024

Preprint

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Electroencephalogram (EEG) signals are produced by neurons of human brain and contain frequencies and electrical properties. It is easy for a Brain to Computer Interface (BCI) system to record EEG signals by using non-invasive methods. Speech imagery (SI) can be used to convert speech imaging into text, researches done so far on SI has made use of multichannel devices. In this work, we propose EEG signal dataset for imagined a/e/i/o/u vowels collected from 5 participants using NeuroSky Mindwave Mobile2 single channel device. Decision Tree (DT), Random Forest (RF), Genetic Algorithm (GA) Machine Learning (ML) classifiers are trained with proposed dataset. For the proposed dataset, the average classification accuracy of DT is found lower in comparison to RF and GA. GA shows better performance for vowel e/o/u resulting accuracy of 80.8%, 82.36%, 81.8% for 70 − 30 data partition, 80.2%, 81.9%, 80.6% for 60 − 40 partition data and 79.8%, 81.12%, 78.36% for 50–50 data partition. Whereas RF shows improved classification accuracy for a/i which is 83.44%, 81.6% for 70 − 30 data partition, 82.2%, 81.2% for 60 − 40 data partition and 81.4%, 80.2% for 50–50 data partition. Some other performance parameters like min. value, max. value of accuracy, standard deviation, sensitivity, specificity, precision, F1 score, false positive rate and receiver operating characteristics are also evaluated and anal- ysed. Research has proven that brain functions remains normal in patients with vocal disorders. Completely disabled patients can be equipped with such technol- ogy as this may be one of the best way for them to have access over the essential day to day basic requirement.

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“…The classification of vowels has already been done in previous years too. Majority of researches done so far make use of multi-channel EEG devices (Table 1) [16] [17] [18] [19] [20].…”

Section: Methodsmentioning

confidence: 99%

“…A supervised deep learning model is proposed by Mahapatra and Bhuyan [20]. Here they combine temporal convolution networks and convolution neural networks to build a deep learning model.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Iqbal,

Bhardwaj,

Suhag

et al. 2024

Preprint

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Classification of imagined speech of vowels from EEG signals using multi-headed CNNs feature fusion network

Tiwari,

Goel,

Bhardwaj

2024

Digital Signal Processing

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The role of vowel and consonant onsets in neural tracking of natural speech

Jalilpour Monesi,

Vanthornhout,

Francart

et al. 2024

J. Neural Eng.

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Objective. To investigate how the auditory system processes natural speech, models have been created to relate the electroencephalography (EEG) signal of a person listening to speech to various representations of the speech. Mainly the speech envelope has been used, but also phonetic representations. We investigated to which degree of granularity phonetic representations can be related to the EEG signal. Approach. We used recorded EEG signals from 105 subjects while they listened to fairy tale stories. We utilized speech representations, including onset of any phone, vowel-consonant onsets, broad phonetic class (BPC) onsets, and narrow phonetic class (NPC) onsets, and related them to EEG using forward modeling and match-mismatch tasks. In forward modeling, we used a linear model to predict EEG from speech representations. In the match-mismatch task, we trained a long short term memory (LSTM) based model to determine which of two candidate speech segments matches with a given EEG segment. Main results. Our results show that vowel-consonant onsets outperform onsets of any phone in both tasks, which suggests that neural tracking of the vowel vs. consonant exists in the EEG to some degree. We also observed that vowel (syllable nucleus) onsets exhibit a more consistent representation in EEG compared to syllable onsets. Significance. Finally, our findings suggest that neural tracking previously thought to be associated with broad phonetic classes might actually originate from vowel-consonant onsets rather than the differentiation betweendifferent phonetic classes.

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Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning

Cited by 4 publications

References 35 publications

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Analysis of Machine Learning Models Using Proposed EEG Vowel Dataset

Classification of imagined speech of vowels from EEG signals using multi-headed CNNs feature fusion network

The role of vowel and consonant onsets in neural tracking of natural speech

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