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

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.

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

“…The first aspect of visualization is the time-varying activations collected within the brain waveforms that are displayed in Figure 8. As seen, the µ band of the Group-I individuals contributes the most, except for the MI interval, τ 3 , meaning that the execution of elicited neural responses comprises the brain activity elicited at higher frequencies [57]. Although a similar behavior holds for Group-II individuals, the brain activity at higher β frequencies is not so intense as for the well-performing subjects.…”

Epileptic Seizure Detection Using Deep Learning Based Long Short-Term Memory Networks and Time-Frequency Analysis: a Comparative Investigation in Machine Learning Paradigm

“…𝛿 𝑡𝑝 ×𝛿 𝑡𝑛 −𝛿 𝑓𝑝 ×𝛿 𝑓𝑛 √(𝛿 𝑡𝑝 +𝛿 𝑓𝑝 )(𝛿 𝑡𝑝 +𝛿 𝑓𝑛 )(𝛿 𝑡𝑛 +𝛿 𝑓𝑝 )(𝛿 𝑡𝑛 +𝛿 𝑓𝑛 ) × 100 (5) The symbol 𝛿 with subscript TP, TN, FP, and FN denotes true positive, true negative, false positive, and false negative respectively. MATLAB® software platform has been used to evaluate the performance of all classifiers.…”

“…Recent studies based on modern computational approaches have proved their significance in detecting diseases and disorders related to human body organs. The various machine learning and deep learning techniques were involved not only for screening complex structural and functional deficiencies e.g., breast cancer, brain anomalies, but also track and assist human being to perform daily-life movement-related activities in case they are bedridden [3,4,5,6]. This article investigates long short-term memory (LSTM) deep learning networks for the classification of epileptic EEG signals using time-frequency features.…”

Analysis of instantaneous brain interactions contribution to a motor imagery classification task

“…Hence, the statistical contribution of these interactions is implicit as a CSP improvement and not as a feature. Other features, such as power bands, wavelet coefficients, and auto-regressive models, do not ever use any interactions between electrodes or brain zones (Aggarwal and Chugh, 2019 ; Mohdiwale et al, 2021 ).…”