A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm

PLOS ONE RESEARCH ARTICLE A novel framework for classification of twoclass motor imagery EEG signals using logistic regression classification algorithm Rabia Avais Khan1☯, Nasir Rashid ID1,2☯*, Muhammad Shahzaib1☯, Umar Farooq Malik1, Arshia Arif1, Javaid Iqbal1,2, Mubasher Saleem1, Umar Shahbaz Khan ID1,2, Mohsin Tiwana1,2 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Mechatronics Engineering, National University of Sciences & Technology, Islamabad, Pakistan, 2 Robot Design and Development Lab, National Centre of Robotics and Automation (NCRA), Punjab, Pakistan ☯ These authors contributed equally to this work. * Abstract OPEN ACCESS Citation: Khan RA, Rashid N, Shahzaib M, Malik UF, Arif A, Iqbal J, et al. (2023) A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm. PLoS ONE 18(9): e0276133. https://doi. org/10.1371/journal.pone.0276133 Editor: Saeed Mian Qaisar, Effat University, SAUDI ARABIA Received: March 24, 2022 Accepted: September 29, 2022 Published: September 8, 2023 Copyright: © 2023 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data used are publicly available from https://www.bbci.de/ competition/iv/ and https://www.bbci.de/ competition/iii/. Funding: This work is funded by the Higher Education Commission of Pakistan under grants titled “Establishment of National Centre of Robotics and Automation (DF-1009-31). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Following authors received Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future. PLOS ONE | https://doi.org/10.1371/journal.pone.0276133 September 8, 2023 1 / 18 PLOS ONE remuneration from the funder (Higher Education Commission of Pakistan):- a. Nasir Rashid, PhD b. Javaid Iqbal, Mechatronics Engineering c. Umar Shahbaz Khan, Electrical Engineering d. Mohsin Tiwana, Biomedical Engineering. Competing interests: The authors have declared that no competing interests exist. Novel framework for classification of two-class motor imagery EEG signals Introduction Brain-Computer Interface is a technology that creates a communication channel between the human brain and the external devices by picking up brain signals and translating them into artificial outputs. This system includes collecting data from the human brain, processing it to detect the user’s intent, and then training the system to actuate an external device. Electroencephalography is a non-invasive technique that records brain signals by recognizing the change in brain wave patterns. The EEG signal is often an amalgamation of many base frequencies known to describe the cognitive, affective, or attentional states. These frequencies are based on particular ranges or bands. The EEG signal frequency range is 0–100 Hz, which is divided into five bands delta ‘δ’ (0.5–4 Hz), theta ‘θ’ (4–7 Hz), alpha ‘α’ (8–13 Hz), beta ‘β’ (13– 30 Hz), and gamma ‘γ’ (within and above 35 Hz) [1]. The μ frequency band overlaps with α frequency band, but the first arises in the sensorimotor cortex while the second originates in the occipital and posterior regions of the brain [2]. Brain signals can be recorded non-invasively by various techniques such as functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG) and electroencephalography (EEG), etc., as well as invasively through electrocorticography (ECoG) and microelectrode arrays (MEAs) [3]. For motor imagery (MI) data, EEG is mostly preferred due to its non-invasiveness, low cost, portability, less sensitivity to movement, and good temporal resolution [4]. The brain activity due to MI shows amplitude changes in certain frequency bands, also referred to as variations in sensorimotor rhythms. When a voluntary movement is performed, there is a decrease in amplitude, referred to as event-related desynchronizations (ERD), and after the activity is over, there is an increase in amplitude known as event-related synchronizations (ERS) [5]. The ERD and ERS are known as event-related potential (ERP). The MI-related EEG signals originating in the sensorimotor region of the brain are based on μ (8–12 Hz) and β (14–30 Hz) frequency bands [6, 7]. Brain signals are recorded from different brain regions, but directly using the EEG signals from all the channels would increase noise interference and may decrease the classification performance. Common spatial pattern (CSP) [8] is used to separate the appropriate signal characteristics from raw EEG data and represent them in a form interpretable by a human or a computer. Independent component analysis (ICA) [9] is a common approach for artifact removal. For identifying human brain activity patterns and transl (...truncated)