ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK
Electro encephalography (EEG) can b e used to classify attempted hand movements of Spinal Cord Injured (SCI) patients for improving their quality of life. EEG classifiation with Brain-Computer Interface (BCI) allows individuals who are suffring from the most severe motor disabilities to control a...
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| Format: | Thesis |
| Sprog: | engelsk |
| Udgivet: |
Department of Biomedical Engineering, MIST
2024
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| Online adgang: | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/817 |
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| _version_ | 1868226990694203392 |
|---|---|
| author | SHRESTHA, KUMAR |
| author_browse | SHRESTHA, KUMAR |
| author_facet | SHRESTHA, KUMAR |
| author_sort | SHRESTHA, KUMAR |
| collection | DSpace |
| description | Electro encephalography (EEG) can b e used to classify attempted hand movements
of Spinal Cord Injured (SCI) patients for improving their quality of life. EEG
classifiation with Brain-Computer Interface (BCI) allows individuals who are
suffring from the most severe motor disabilities to control and direct
electromechanical devices. Practical applications of BCI system require improved
classifiation p erformance for attempted hand movements of SCI patients. This
research aims to develop a hybrid CNN-LSTM architecture for multichannel EEG
signal classifiation, optimize its hyp erparameters, and validate p erformance metrics
for improved classifiation p erformance. EEG data acquired from SCI patients go
through fitration, downsampling, and artifact removal, followed by the generation of
Time-Frequency Representation (TFR) of EEG data. Spatial enco ding is done by
arranging TFR data in a 2D array corresp onding to spatial layout of electro des.
Spatial enco ded TFR data is then fed to CNN-LSTM (Convolutional Neural
Network – Long Short Term Memory) N etwork to obtain the fial classifiation
output. Sp ectral, spatial, and temp oral information is vital in EEG classifiation.
Novelty in the design of Hybrid CNN-LSTM network architecture is that it can
learn to extract sp ectral, spatial, and temp oral information and then use these
learned information to improve fial classifiation p erformance. Hybrid CNN-LSTM
architecture achieved a classifiation accuracy of 92.36% using 10% of the dataset
for training and 90% of the dataset for testing. This result shows 47.363% increased
classifiation accuracy as compared to related study while also having improved
generalizability. H ybrid CNN-LSTM network for EEG classifiation is able: to deal
with artifacts in EEG data without signifiant loss in classifiation p erformance; to
extract sp ectral information, spatial information, and temp oral information of
valuable neural impulses from EEG data. The steps of EEG classifiation used in
this research can b e used not only for attempted movements of SCI patients but also
for other neurological diseases, neuroscience applications, mental workload,
neuromarketing, and biometrics. |
| format | Thesis |
| id | oai:localhost:123456789-817 |
| institution | My University |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Biomedical Engineering, MIST |
| publisherStr | Department of Biomedical Engineering, MIST |
| record_format | dspace |
| spelling | oai:localhost:123456789-8172024-06-10T06:27:42Z ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK SHRESTHA, KUMAR Electro encephalography (EEG) can b e used to classify attempted hand movements of Spinal Cord Injured (SCI) patients for improving their quality of life. EEG classifiation with Brain-Computer Interface (BCI) allows individuals who are suffring from the most severe motor disabilities to control and direct electromechanical devices. Practical applications of BCI system require improved classifiation p erformance for attempted hand movements of SCI patients. This research aims to develop a hybrid CNN-LSTM architecture for multichannel EEG signal classifiation, optimize its hyp erparameters, and validate p erformance metrics for improved classifiation p erformance. EEG data acquired from SCI patients go through fitration, downsampling, and artifact removal, followed by the generation of Time-Frequency Representation (TFR) of EEG data. Spatial enco ding is done by arranging TFR data in a 2D array corresp onding to spatial layout of electro des. Spatial enco ded TFR data is then fed to CNN-LSTM (Convolutional Neural Network – Long Short Term Memory) N etwork to obtain the fial classifiation output. Sp ectral, spatial, and temp oral information is vital in EEG classifiation. Novelty in the design of Hybrid CNN-LSTM network architecture is that it can learn to extract sp ectral, spatial, and temp oral information and then use these learned information to improve fial classifiation p erformance. Hybrid CNN-LSTM architecture achieved a classifiation accuracy of 92.36% using 10% of the dataset for training and 90% of the dataset for testing. This result shows 47.363% increased classifiation accuracy as compared to related study while also having improved generalizability. H ybrid CNN-LSTM network for EEG classifiation is able: to deal with artifacts in EEG data without signifiant loss in classifiation p erformance; to extract sp ectral information, spatial information, and temp oral information of valuable neural impulses from EEG data. The steps of EEG classifiation used in this research can b e used not only for attempted movements of SCI patients but also for other neurological diseases, neuroscience applications, mental workload, neuromarketing, and biometrics. 2024-06-10T06:27:42Z 2024-06-10T06:27:42Z 2023-03 Thesis http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/817 en application/pdf Department of Biomedical Engineering, MIST |
| spellingShingle | SHRESTHA, KUMAR ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK |
| title | ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK |
| title_full | ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK |
| title_fullStr | ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK |
| title_full_unstemmed | ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK |
| title_short | ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK |
| title_sort | attempted movement classification of spinal cord injured patients combining cnn and lstm network |
| url | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/817 |
| work_keys_str_mv | AT shresthakumar attemptedmovementclassificationofspinalcordinjuredpatientscombiningcnnandlstmnetwork |