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

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11182
Author(s):  
Jothi Letchumy Mahendra Kumar ◽  
Mamunur Rashid ◽  
Rabiu Muazu Musa ◽  
Mohd Azraai Mohd Razman ◽  
Norizam Sulaiman ◽  
...  
Keyword(s):  
Random Forest ◽  
Transfer Learning ◽  
Life Quality ◽  
Validation Dataset ◽  
Eeg Signals ◽  
Stroke Patients ◽  
Classifier Performance ◽  
Search Approach ◽  
Bci System

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.

scholarly journals An Evaluation of Different Fast Fourier Transform - Transfer Learning Pipelines for the Classification of Wink-based EEG Signals

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Jothi Letchumy Mahendra Kumar ◽  
Mamunur Rashid ◽  
Rabiu Muazu Musa ◽  
Mohd Azraai Mohd Razman ◽  
Norizam Sulaiman ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Transfer Learning ◽  
Test Accuracy ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Physical Limitations ◽  
Search Approach ◽  
Electroencephalogram Eeg

Brain Computer-Interfaces (BCI) offers a means of controlling prostheses for neurological disorder patients, primarily owing to their inability to control such devices due to their inherent physical limitations. More often than not, the control of such devices exploits the use of Electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features is often a laborious undertaking. The use of Transfer Learning (TL) has been demonstrated to be able to mitigate the issue. However, the employment of such a method towards BCI applications, particularly with regards to EEG signals are limited. The present study aims to assess the effectiveness of a number of DenseNet TL models, viz. DenseNet169, DenseNet121 and DenseNet201 in extracting features for the classification of wink-based EEG signals. The extracted features are then classified through an optimised Random Forest (RF) classifier. The raw EEG signals are transformed into a spectrogram image via Fast Fourier Transform (FFT) before it was fed into selected TL models. The dataset was split with a stratified ratio of 60:20:20 into train, test, and validation datasets, respectively. The hyperparameters of the RF model was optimised through the grid search approach that utilises the five-fold cross-validation technique. It was established from the study that amongst the DenseNet pipelines evaluated, the DenseNet169 performed the best with an overall validation and test accuracy of 89%. The findings of the present investigation could facilitate BCI applications, e.g., for a grasping exoskeleton.

The Animal Classification: An Evaluation of Different Transfer Learning Pipeline

Mekatronika ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 27-31
Author(s):  
Ken-ji Ee ◽  
Ahmad Fakhri Bin Ab. Nasir ◽  
Anwar P. P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman ◽  
Nur Hafieza Ismail
Keyword(s):  
Transfer Learning ◽  
Classification System ◽  
Cross Validation ◽  
Support Vector ◽  
Svm Classifier ◽  
Average Classification Accuracy ◽  
Validation Technique ◽  
Search Approach ◽  
Fold Cross Validation

The animal classification system is a technology to classify the animal class (type) automatically and useful in many applications. There are many types of learning models applied to this technology recently. Nonetheless, it is worth noting that the extraction of the features and the classification of the animal features is non-trivial, particularly in the deep learning approach for a successful animal classification 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 animal classification applications are somewhat limited. The present study aims to determine a suitable TL-conventional classifier pipeline for animal classification. The VGG16 and VGG19 were used in extracting features and then coupled with either k-Nearest Neighbour (k-NN) or Support Vector Machine (SVM) classifier. Prior to that, a total of 4000 images were gathered consisting of a total of five classes which are cows, goats, buffalos, dogs, and cats. The data was split into the ratio of 80:20 for train and test. The classifiers hyper parameters are tuned by the Grids Search approach that utilises the five-fold cross-validation technique. It was demonstrated from the study that the best TL pipeline identified is the VGG16 along with an optimised SVM, as it was able to yield an average classification accuracy of 0.975. The findings of the present investigation could facilitate animal classification application, i.e. for monitoring animals in wildlife.

scholarly journals Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Jing-Shan Huang ◽  
Wan-Shan Liu ◽  
Bin Yao ◽  
Zhan-Xiang Wang ◽  
Si-Fang Chen ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Recognition Accuracy ◽  
Wavelet Packet ◽  
Eeg Signals ◽  
Convolutional Networks ◽  
Learning Classifier ◽  
Signal Features ◽  
Electroencephalogram Eeg ◽  
Bci System

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.

The Classification of Wink-Based EEG Signals: The Identification on Efficiency of Transfer Learning Models by Means of kNN Classifier

2021 ◽  
pp. 205-213
Author(s):  
Jothi Letchumy Mahendra Kumar ◽  
Mamunur Rashid ◽  
Rabiu Muazu Musa ◽  
Mohd Azraai Mohd Razman ◽  
Norizam Sulaiman ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Learning Models ◽  
Eeg Signals ◽  
Knn Classifier

The Classification of Wink-Based EEG Signals: An Evaluation of Different Transfer Learning Models for Feature Extraction

2021 ◽  
pp. 61-70
Author(s):  
Jothi Letchumy Mahendra Kumar ◽  
Mamunur Rashid ◽  
Rabiu Muazu Musa ◽  
Mohd Azraai Mohd Razman ◽  
Norizam Sulaiman ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Learning Models ◽  
Eeg Signals

scholarly journals Transfer Learning for an Automated Detection System of Fractures in Patients with Maxillofacial Trauma

2021 ◽  
Vol 11 (14) ◽  
pp. 6293
Author(s):  
Maria Amodeo ◽  
Vincenzo Abbate ◽  
Pasquale Arpaia ◽  
Renato Cuocolo ◽  
Giovanni Dell’Aversana Orabona ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Human Error ◽  
Detection System ◽  
Fracture Probability ◽  
Ct Scans ◽  
Validation Dataset ◽  
Fracture Detection ◽  
Maxillofacial Fractures ◽  
Diagnostic Delays

An original maxillofacial fracture detection system (MFDS), based on convolutional neural networks and transfer learning, is proposed to detect traumatic fractures in patients. A convolutional neural network pre-trained on non-medical images was re-trained and fine-tuned using computed tomography (CT) scans to produce a model for the classification of future CTs as either “fracture” or “noFracture”. The model was trained on a total of 148 CTs (120 patients labeled with “fracture” and 28 patients labeled with “noFracture”). The validation dataset, used for statistical analysis, was characterized by 30 patients (5 with “noFracture” and 25 with “fracture”). An additional 30 CT scans, comprising 25 “fracture” and 5 “noFracture” images, were used as the test dataset for final testing. Tests were carried out both by considering the single slices and by grouping the slices for patients. A patient was categorized as fractured if two consecutive slices were classified with a fracture probability higher than 0.99. The patients’ results show that the model accuracy in classifying the maxillofacial fractures is 80%. Even if the MFDS model cannot replace the radiologist’s work, it can provide valuable assistive support, reducing the risk of human error, preventing patient harm by minimizing diagnostic delays, and reducing the incongruous burden of hospitalization.

scholarly journals A Study of the Classification of Motor Imagery Signals using Machine Learning Tools

2021 ◽  
Author(s):  
Anam Hashmi ◽  
Bilal Alam Khan ◽  
Omar Farooq
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Random Forest ◽  
Random Forest Algorithm ◽  
Eeg Signals ◽  
Relaxation State ◽  
Wavelet Transform Analysis ◽  
Imagined Movement

In this paper, we propose a system for the purpose of classifying Electroencephalography (EEG) signals associated with imagined movement of right hand and relaxation state using machine learning algorithm namely Random Forest Algorithm. The EEG dataset used in this research was created by the University of Tubingen, Germany. EEG signals associated with the imagined movement of right hand and relaxation state were processed using wavelet transform analysis with Daubechies orthogonal wavelet as the mother wavelet. After the wavelet transform analysis, eight features were extracted. Subsequently, a feature selection method based on Random Forest Algorithm was employed giving us the best features out of the eight proposed features. The feature selection stage was followed by classification stage in which eight different models combining the different features based on their importance were constructed. The optimum classification performance of 85.41% was achieved with the Random Forest classifier. This research shows that this system of classification of motor movements can be used in a Brain Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

Sign in / Sign up

Export Citation Format

Share Document