FEW-SHOT CLASSIFICATION OF EEG WITH QUASI-INDUCTIVE TRANSFER LEARNING
(2022) In Bachelor's Theses in Mathematicas Sciences MASK11 20222Mathematical Statistics
- Abstract
- Brain-computer interfaces (BCIs) are devices that enable people with disabilities to use their thoughts to control external devices and restore or improve
their bodily functions. One important aspect of BCIs is the classification of
electroencephalography (EEG) signals, which measure brain activity and can
be difficult to interpret. To address this challenge, we use time-frequency
transformations to convert EEG signals into images and employ pre-trained
deep convolutional neural networks (CNNs) to classify the images based on
whether the subject heard a sound from the left or the right ear. We investigate whether transfer learning, a technique that involves using a pre-trained
model on a related task as the starting point for... (More) - Brain-computer interfaces (BCIs) are devices that enable people with disabilities to use their thoughts to control external devices and restore or improve
their bodily functions. One important aspect of BCIs is the classification of
electroencephalography (EEG) signals, which measure brain activity and can
be difficult to interpret. To address this challenge, we use time-frequency
transformations to convert EEG signals into images and employ pre-trained
deep convolutional neural networks (CNNs) to classify the images based on
whether the subject heard a sound from the left or the right ear. We investigate whether transfer learning, a technique that involves using a pre-trained
model on a related task as the starting point for training on a new task, is
effective even when the source and target domains are very different. The
best classification result achieved was 61.7%, using EfficientNet V2 tuned on
5 different test-subjects, and tuned on the target subject. (Less) - Popular Abstract
- Brain-computer interfaces (BCIs) are devices that enable people with disabilities to use their thoughts to control external devices and restore or improve
their bodily functions. One important aspect of BCIs is the classification of
electroencephalography (EEG) signals, which measure brain activity and can
be difficult to interpret. To address this challenge, we use time-frequency
transformations to convert EEG signals into images and employ pre-trained
deep convolutional neural networks (CNNs), networks specialized in image classification, to classify the images based on
whether the subject heard a sound from the left or the right ear. We investigate whether transfer learning, a technique that involves using a pre-trained
model... (More) - Brain-computer interfaces (BCIs) are devices that enable people with disabilities to use their thoughts to control external devices and restore or improve
their bodily functions. One important aspect of BCIs is the classification of
electroencephalography (EEG) signals, which measure brain activity and can
be difficult to interpret. To address this challenge, we use time-frequency
transformations to convert EEG signals into images and employ pre-trained
deep convolutional neural networks (CNNs), networks specialized in image classification, to classify the images based on
whether the subject heard a sound from the left or the right ear. We investigate whether transfer learning, a technique that involves using a pre-trained
model with knowledge of one task to more easily acquire knowledge of a related task, is effective even when the source and target domains are very different. The
best classification result achieved was 61.7%, using EfficientNet V2 tuned on
5 different test-subjects, and tuned on the target subject. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9104237
- author
- Duedahl, Mathias LU
- supervisor
- organization
- course
- MASK11 20222
- year
- 2022
- type
- M2 - Bachelor Degree
- subject
- keywords
- Few-Shot Learning, Transfer Learning, EEG Classification, Deep Convolutional Neural Network, DNN, CNN, EEG, FSL, TL
- publication/series
- Bachelor's Theses in Mathematicas Sciences
- report number
- LUNFMS-4068-2022
- ISSN
- 1654-6229
- other publication id
- 2022:K22
- language
- English
- id
- 9104237
- date added to LUP
- 2023-01-17 14:21:11
- date last changed
- 2023-01-17 14:21:11
@misc{9104237, abstract = {{Brain-computer interfaces (BCIs) are devices that enable people with disabilities to use their thoughts to control external devices and restore or improve their bodily functions. One important aspect of BCIs is the classification of electroencephalography (EEG) signals, which measure brain activity and can be difficult to interpret. To address this challenge, we use time-frequency transformations to convert EEG signals into images and employ pre-trained deep convolutional neural networks (CNNs) to classify the images based on whether the subject heard a sound from the left or the right ear. We investigate whether transfer learning, a technique that involves using a pre-trained model on a related task as the starting point for training on a new task, is effective even when the source and target domains are very different. The best classification result achieved was 61.7%, using EfficientNet V2 tuned on 5 different test-subjects, and tuned on the target subject.}}, author = {{Duedahl, Mathias}}, issn = {{1654-6229}}, language = {{eng}}, note = {{Student Paper}}, series = {{Bachelor's Theses in Mathematicas Sciences}}, title = {{FEW-SHOT CLASSIFICATION OF EEG WITH QUASI-INDUCTIVE TRANSFER LEARNING}}, year = {{2022}}, }