Design of EEG based thought identification system using EMD & deep neural network
Abstract Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as i...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-64961-1 |
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| author | Rahul Agrawal Chetan Dhule Garima Shukla Sofia Singh Urvashi Agrawal Najah Alsubaie Mohammed S. Alqahtani Mohamed Abbas Ben Othman Soufiene |
| author_facet | Rahul Agrawal Chetan Dhule Garima Shukla Sofia Singh Urvashi Agrawal Najah Alsubaie Mohammed S. Alqahtani Mohamed Abbas Ben Othman Soufiene |
| author_sort | Rahul Agrawal |
| collection | DOAJ |
| description | Abstract Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods. |
| format | Article |
| id | doaj-art-ba5eef2aafd64317ae3e585c9a13d394 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ba5eef2aafd64317ae3e585c9a13d3942025-08-20T02:49:57ZengNature PortfolioScientific Reports2045-23222024-11-0114111810.1038/s41598-024-64961-1Design of EEG based thought identification system using EMD & deep neural networkRahul Agrawal0Chetan Dhule1Garima Shukla2Sofia Singh3Urvashi Agrawal4Najah Alsubaie5Mohammed S. Alqahtani6Mohamed Abbas7Ben Othman Soufiene8Department of Data Science, IoT, Cybersecurity (DIC), G H Raisoni College of EngineeringDepartment of Data Science, IoT, Cybersecurity (DIC), G H Raisoni College of EngineeringDepartment of Computer Science Engineering, Amity School of Engineering & Technology, Amity UniversityDepartment of AI, Amity School of Engineering & Technology, Amity UniversityDepartment of Electronics & Telecommunication Engineering, Jhulelal Institute of TechnologyDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityRadiological Sciences Department, College of Applied Medical Sciences, King Khalid UniversityElectrical Engineering Department, College of Engineering, King Khalid UniversityPRINCE Laboratory Research, ISITcom, Hammam Sousse, University of SousseAbstract Biological communication system for neurological disorder patients is similar to the Brain Computer Interface in a way that it facilitates the connection to the outside world in real time. The interdisciplinary field of Electroencephalogram based message depiction is gaining importance as it assists the paralysed person to communicate. In the proposed method a novel approach of feature extraction is done by Empirical Mode Decomposition on non- stationary & non-linear kind of EEG signal. EMD helps in the effective time frequency analysis by disintegrating the EEG signal in the form of six Intrinsic Mode Functions with help of the frequency components. In all nine features are extracted from the decomposed IMFs so as to predict the states or messages of the patient. The above computed features are then served to the Deep Neural Network to perform the classification. The performance of suggested method is studied through applying it to the acquired database generated by the designed hardware as well as also in real time message depiction. The maximum classification accuracy 97% for the acquired database & 85% in real time are obtained respectively by comparative analysis. The command messages generated from the proposed system helps the person suffering from neurological disorder to establish the communication link with the outside world in an efficient way. Thus, the proposed novel method shows better performance in real time message depiction purpose as related to other existing methods.https://doi.org/10.1038/s41598-024-64961-1Brain computer interface (BCI)Electroencephalography (EEG)Central nervous system (CNS)Empirical Mode decomposition (EMD)Deep Neural network (DNN) etc |
| spellingShingle | Rahul Agrawal Chetan Dhule Garima Shukla Sofia Singh Urvashi Agrawal Najah Alsubaie Mohammed S. Alqahtani Mohamed Abbas Ben Othman Soufiene Design of EEG based thought identification system using EMD & deep neural network Scientific Reports Brain computer interface (BCI) Electroencephalography (EEG) Central nervous system (CNS) Empirical Mode decomposition (EMD) Deep Neural network (DNN) etc |
| title | Design of EEG based thought identification system using EMD & deep neural network |
| title_full | Design of EEG based thought identification system using EMD & deep neural network |
| title_fullStr | Design of EEG based thought identification system using EMD & deep neural network |
| title_full_unstemmed | Design of EEG based thought identification system using EMD & deep neural network |
| title_short | Design of EEG based thought identification system using EMD & deep neural network |
| title_sort | design of eeg based thought identification system using emd deep neural network |
| topic | Brain computer interface (BCI) Electroencephalography (EEG) Central nervous system (CNS) Empirical Mode decomposition (EMD) Deep Neural network (DNN) etc |
| url | https://doi.org/10.1038/s41598-024-64961-1 |
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