scholarly journals Investigating Feature Ranking Methods for Sub-Band and Relative Power Features in Motor Imagery Task Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Samrudhi Mohdiwale ◽  
Mridu Sahu ◽  
G. R. Sinha ◽  
Humaira Nisar
Keyword(s):  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Feature Space ◽  
Feature Ranking ◽  
Imagery Task ◽  
Relative Power ◽  
Chi Square ◽  
Emergency Situations ◽  
Task Classification ◽  
And Performance

Interpreting the brain commands is now easier using brain-computer interface (BCI) technologies. Motor imagery (MI) signal detection is one of the BCI applications, where the movements of the hand and feet can be recognized via brain commands that can be further used to handle emergency situations. Design of BCI techniques encountered challenges of BCI illiteracy, poor signal to noise ratio, intersubject variability, complexity, and performance. The automated models designed for emergency should have lesser complexity and higher performance. To deal with the challenges related to the complexity performance tradeoff, the frequency features of brain signal are utilized in this study. Feature matrix is created from the power of brain frequencies, and newly proposed relative power features are used. Analysis of the relative power of alpha sub-band to beta, gamma, and theta sub-band has been done. These proposed relative features are evaluated with the help of different classifiers. For motor imagery classification, the proposed approach resulted in a maximum accuracy of 93.51% compared to other existing approaches. To check the significance of newly added features, feature ranking approaches, namely, mutual information, chi-square, and correlation, are used. The ranking of features shows that the relative power features are significant for MI task classification. The chi-square provides the best tradeoff between accuracy and feature space. We found that the addition of relative power features improves the overall performance. The proposed models could also provide quick response having reduced complexity.

MEL-FREQUENCY BAND STRUCTURE BASED FEATURES FOR MOTOR IMAGERY TASK CLASSIFICATION

2015 ◽  
Vol 76 (12) ◽  
Author(s):  
Paulraj M. P. ◽  
Jackie Teh
Keyword(s):  
Band Structure ◽  
Motor Imagery ◽  
Frequency Band ◽  
Research Work ◽  
Life Time ◽  
Imagery Task ◽  
Eeg Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Classification Rate ◽  
Task Classification

Differentially enabled communities face much difficulties and challenges in their life time while commuting from one place to another. Power wheelchairs were designed to aid the movement of these differentially enabled subjects and a Brain Computer Interface can also be applied to replace the existing conventional joystick method of controlling the movement of a wheelchair without using hands. In this research work, a simple protocol is proposed to record the EEG signals emanated from a subject while the subject performed four different kinesthetic motor imagery tasks. The noise present in the EEG signals are removed and three different feature sets, namely, power spectral density, Mel-frequency cepstral coefficients and Mel-frequency band structure based energy features are extracted. The extracted features are then associated to the type of motor imagery tasks and three multi-layer Perceptrons trained with Levenberg-Marquardt method are developed. The performance of the three Perceptron models are evaluated in term of classification rate and compared. From the results, it is observed that the Perceptron model trained with Mel-frequency band structure based features has yielded a higher classification accuracy for all 5 subjects, which is between 92.64-97.72%. The obtained result clearly indicates that the Mel-frequency band structure based features has potential to classify the four different motor imagery tasks. 

scholarly journals COMPARISONS BETWEEN MOTOR AREA EEG AND ALL-CHANNELS EEG FOR TWO ALGORITHMS IN MOTOR IMAGERY TASK CLASSIFICATION

2014 ◽  
Vol 26 (03) ◽  
pp. 1450040 ◽  
Author(s):  
Siuly ◽  
Yan Li ◽  
Peng Wen
Keyword(s):  
Motor Imagery ◽  
Binary Logistic Regression ◽  
Classification Performance ◽  
Least Square ◽  
Motor Area ◽  
Support Vector ◽  
Imagery Task ◽  
Task Classification ◽  
Svm Algorithm ◽  
The Brain

This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM-based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.

scholarly journals Motor imagery task classification using transformation based features

2017 ◽  
Vol 33 ◽  
pp. 213-219 ◽  
Author(s):  
Aida Khorshidtalab ◽  
Momoh J.E. Salami ◽  
Rini Akmeliawati
Keyword(s):  
Motor Imagery ◽  
Imagery Task ◽  
Task Classification
Sign in / Sign up

Export Citation Format

Share Document