Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning
The paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagine...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2022/1374880 |
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| author | Nrushingh Charan Mahapatra Prachet Bhuyan |
| author_facet | Nrushingh Charan Mahapatra Prachet Bhuyan |
| author_sort | Nrushingh Charan Mahapatra |
| collection | DOAJ |
| description | The paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. Related research work in the field of imagined speech has revealed that imagined speech decoding performance and accuracy require attention to further improve. The evolution of deep learning technology increases the likelihood of decoding imagined speech from EEG signals with enhanced performance. We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. The experiment was carried out using an open-access dataset of fifteen subjects’ imagined speech multichannel signals of vowels and words. The raw multichannel EEG signals of multiple subjects were processed using discrete wavelet transformation technique. The model was trained and evaluated using the preprocessed signals, and the model hyperparameters were adjusted to achieve higher accuracy in the classification of imagined speech. The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. The results of the study indicate that individuals with speech difficulties might well be able to leverage a noninvasive EEG-based imagined speech brain-computer interface system as one of the long-term alternative artificial verbal communication mediums. |
| format | Article |
| id | doaj-art-e165df1ab53e49259cba0fabb3354bbd |
| institution | OA Journals |
| issn | 1687-5907 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-e165df1ab53e49259cba0fabb3354bbd2025-08-20T02:21:01ZengWileyAdvances in Human-Computer Interaction1687-59072022-01-01202210.1155/2022/1374880Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep LearningNrushingh Charan Mahapatra0Prachet Bhuyan1Intel Technology India Pvt LtdSchool of Computer EngineeringThe paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. Related research work in the field of imagined speech has revealed that imagined speech decoding performance and accuracy require attention to further improve. The evolution of deep learning technology increases the likelihood of decoding imagined speech from EEG signals with enhanced performance. We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. The experiment was carried out using an open-access dataset of fifteen subjects’ imagined speech multichannel signals of vowels and words. The raw multichannel EEG signals of multiple subjects were processed using discrete wavelet transformation technique. The model was trained and evaluated using the preprocessed signals, and the model hyperparameters were adjusted to achieve higher accuracy in the classification of imagined speech. The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. The results of the study indicate that individuals with speech difficulties might well be able to leverage a noninvasive EEG-based imagined speech brain-computer interface system as one of the long-term alternative artificial verbal communication mediums.http://dx.doi.org/10.1155/2022/1374880 |
| spellingShingle | Nrushingh Charan Mahapatra Prachet Bhuyan Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning Advances in Human-Computer Interaction |
| title | Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning |
| title_full | Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning |
| title_fullStr | Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning |
| title_full_unstemmed | Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning |
| title_short | Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning |
| title_sort | multiclass classification of imagined speech vowels and words of electroencephalography signals using deep learning |
| url | http://dx.doi.org/10.1155/2022/1374880 |
| work_keys_str_mv | AT nrushinghcharanmahapatra multiclassclassificationofimaginedspeechvowelsandwordsofelectroencephalographysignalsusingdeeplearning AT prachetbhuyan multiclassclassificationofimaginedspeechvowelsandwordsofelectroencephalographysignalsusingdeeplearning |