Abstract
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.
Highlights
Introduction and BackgroundBrain activates the sensory motor rhythm for virtual motor movements such as hand or feet; the actual motor movement of the body parts is not essential. e activation properties of brain correlate the activities with the motor movements, which help in different emergency situations to provide quick response in the system [1, 2]
Amyotrophic lateral sclerosis (ALS) is one of the serious diseases of brain, where the patient loses their control over the body and only the mind is active. e brain-computer interface (BCI) technology designed for motor imagery task can assist the patients in communication [3]
Decision tree (DT), fine k-nearest neighbor (KNN), weighted KNN (WKNN), quadratic support vector machine (QSVM), and random forest, are used for classification. e results obtained from classification are shown and discussed in Section 3. e results obtained after classification are analyzed based on the classification accuracy of classifier, precision, recall, and F1-score [34]. e comparative analysis of the current study with different existing approaches is presented
Summary
Introduction and BackgroundBrain activates the sensory motor rhythm for virtual motor movements such as hand or feet; the actual motor movement of the body parts is not essential. e activation properties of brain correlate the activities with the motor movements, which help in different emergency situations to provide quick response in the system [1, 2]. E activation properties of brain correlate the activities with the motor movements, which help in different emergency situations to provide quick response in the system [1, 2]. E brain-computer interface (BCI) technology designed for motor imagery task can assist the patients in communication [3]. BCI technology enables the translation of brain commands of motor actions to read the brain signal and is considered as effective method for providing faster and accurate response [4, 5]. E structure of brain consists of four lobes, which are frontal, parietal, occipital, and temporal lobe. Frontal lobe is active when emotion, problem solving, speech, and movement related tasks are performed; and the parietal lobe is responsible for sensation, taste, speech, reading, and so forth. Occipital lobe is responsible for vision, visual stimuli, and interpretation
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