Realising transfer learning through convolutional neural network and support vector machine for mental task classification

Electroencephalogram (EEG) measures brainwaves that have been the widely used modality for brain–computer interface (BCI) applications. EEG signal with machine learning has gained substantial success in the BCI. However, the availability of limited training data, appropriate model selection and high...

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Bibliographic Details
Published in:Electronics letters Vol. 56; no. 25; pp. 1375 - 1378
Main Authors: Singh, D, Singh, S
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.12.2020
Subjects:
BCI
brainwaves
brain‐computer interface applications
brain‐computer interfaces
classification accuracy
conventional machine learning approaches
convolutional neural nets
convolutional neural network‐based approach
data availability
data scarcity
diverse feature extraction
EEG signal
EEG‐based classification
electroencephalogram
electroencephalography
false‐positive rates
feature extraction
learning (artificial intelligence)
medical signal processing
mental task classification model
misclassification ratio
model selection
optimal network
pre‐trained network
Resnet18
Resnet50
signal classification
Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications
support vector machine
support vector machines
training data
transfer learning model
Vgg16
Vgg19
brain-computer interfaces
misclassification ratio
Resnet50
classification accuracy
diverse feature extraction
brain-computer interface applications
mental task classification model
data scarcity
optimal network
feature extraction
pre-trained network
data availability
training data
conventional machine learning approaches
learning (artificial intelligence)
EEG-based classification
electroencephalography
brainwaves
support vector machines
model selection
BCI
false-positive rates
EEG signal
transfer learning model
electroencephalogram
signal classification
medical signal processing
Vgg19
support vector machine
Resnet18
Vgg16
convolutional neural nets
convolutional neural network-based approach
ISSN:0013-5194, 1350-911X, 1350-911X
Online Access:Get full text
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Description
Summary:Electroencephalogram (EEG) measures brainwaves that have been the widely used modality for brain–computer interface (BCI) applications. EEG signal with machine learning has gained substantial success in the BCI. However, the availability of limited training data, appropriate model selection and high false-positive rates are the challenges that need immediate attention. Therefore, in this Letter, the authors present a mental task classification model based on the notion of transfer learning that addresses the issue of data scarcity, model selection and misclassification ratio. In the framework, the proposed model uses pre-trained network for the extraction of diverse feature and classify using support vector machine. The authors employed four pre-trained networks to identify the optimal network for the proposed framework: Vgg16, Vgg19, Resnet18 and Resnet50. The highest classification accuracy of 86.85% (using Resnet50) was achieved using transfer learning. Comparison results showed that convolutional neural network-based approach outperformed conventional machine learning approaches and hence it can be concluded that the EEG-based classification of the mental task using transfer learning model could be used in developing a superior model despite the limited data availability.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2020.2632