CWT Based Transfer Learning for Motor Imagery Classification for Brain computer Interfaces

Kant, Piyush; Laskar, Shahedul Haque; Hazarika, Jupitara; Mahamune, Rupesh

doi:10.1016/j.jneumeth.2020.108886

Cited by 75 publications

(38 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through the confusion matrix, it can observed that there is no misclassification that transpired in the classification of the validation dataset. The efficacy of pre-trained CNN models have been demonstrated in the literature; for instance, Kant et al (2020) Left-and Right-hand movements. It was shown from the study that the employment of such a technique could achieve a CA of 97.06%.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Kumar

Rashid

Musa

et al. 2021

PeerJ

View full text Add to dashboard Cite

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Kant et al (2020) utilised a Continuous Wavelet Transform (CWT) algorithm for the classification process of Motor Imagery (MI) EEG signals. Different TL models with tuned fully connected layers were evaluated in classifying the EEG signals.…”

mentioning

confidence: 99%

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Kumar

Rashid

Musa

et al. 2021

PeerJ

View full text Add to dashboard Cite

show abstract

“…The performance measures obtained through the test dataset for all the four pipelines evaluated are listed in Table 3. The efficacy of pre-trained CNN models have been demonstrated in the literature; for instance, Kant et al implemented a CWT algorithm to classify motor imagery signals by means of transfer learning models (Kant et al, 2020). The digital EEG signals were converted into twodimensional scalogram images that were fed into different pre-trained CNN models such as AlexNet, VGG16 and VGG19 to recognise the motor imagery signals of the Left-and Righthand movements.…”

Section: Resultsmentioning

confidence: 99%

Peer Review #1 of "The classification of EEG-based winking signals: a transfer learning and random forest pipeline (v0.1)"

2021

View full text Add to dashboard Cite

Brain Computer-Interfaces (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly with regards to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable

show abstract

“…Therefore, people are also trying to find more distinguishing features to facilitate EEG decoding. Fast Fourier transform (FFT) is used to convert the original EEG signal to a frequency representation, and continuous wavelet transform (CWT) is used for time-frequency features [24], [25]. Among these strategies, common spatial pattern (CSP), which focus on spatial features, is the most widely recognized [26].…”

Section: Introductionmentioning

confidence: 99%

Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

Song,

Jia,

Yang

et al. 2021

Preprint

View full text Add to dashboard Cite

At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG paradigms with a strong overall relationship. Regarding this issue, we propose a novel EEG decoding method that mainly relies on the attention mechanism. The EEG data is firstly preprocessed and spatially filtered. And then, we apply attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features. The most crucial step is to slice the data in the time dimension for attention transforming, and finally obtain a highly distinguishable representation. At this time, global averaging pooling and a simple fully-connected layer are used to classify different categories of EEG data. Experiments on two public datasets indicate that the strategy of attention transforming effectively utilizes spatial and temporal features. And we have reached the level of the state-of-the-art in multi-classification of EEG, with fewer parameters. As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field. It has good potential to promote the practicality of brain-computer interface (BCI). The source code can be found at: https://github.com/anranknight/EEG-Transformer.

show abstract

CWT Based Transfer Learning for Motor Imagery Classification for Brain computer Interfaces

Cited by 75 publications

References 24 publications

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Peer Review #1 of "The classification of EEG-based winking signals: a transfer learning and random forest pipeline (v0.1)"

Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

Contact Info

Product

Resources

About