A Comparison Study on EEG Signal Processing Techniques Using Motor Imagery EEG Data

Abstract

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work, we provide a review of various existing techniques for the identification of motor imagery (MI) tasks. More specifically, we perform a comparison between Common Spatial Patterns (CSP) related features and features based on Power Spectral Density (PSD) techniques. Furthermore, for the identification of MI tasks, two well-known classifiers are used, the Linear Discriminant Analysis (LDA) and the Support Vector Machines (SVM). Our results confirm that PSD features demonstrate the most consistent robustness and effectiveness in extracting patterns for accurately discriminating between left and right MI tasks.

Classification of Non-Discriminant ERD/ERS Comprising Motor Imagery Electroencephalography Signals

“…Based on the classification results, the type of task is identified. Famous classification approaches used in literature are Linear Discriminant Analysis (LDA) [12] [13], Support Vector Machines (SVM) [12], k-nearest neighbors, Logistic Regression (LR) [14], Quadratic Classifiers [15], Recurrent Neural Network (RNN) [16]. Some other BCI uses feature extraction, selection, and classification as one block, in deep learning [6].…”

“…In recent years, MI EEG signals were classified with Subject Specific Decision Tree (SSDT) [22], Multi Scale Filter Bank Convolutional Neural Network (MSFBCNN) [23], Sequential Backward Floating Selection (SBFS) as feature selection and Naïve Bayesian Parzen window (NBPW) used as classification method [24]. Some authors claimed that Power Spectral Density (PSD) is most effective in extracting patterns for classification MI EEG data [13]. Support Vector Machines (SVM) is very famous in EEG classification [14].…”

“…According to statements of famous researchers "It is impossible to distinguish between left and right foot motor imagery, or between the movements of particular fingers because the cortical areas associated with these distinct movements are too small to generate Discriminative ERD and ERS signals" [13]. Therefore it is required to study how the classification of these subjects can be improved, which have non-discriminative ERD/ERS signals.…”

“…On the other hand, if fi is negative and large then the probability of belonging to C2 is high. If fi is close to zero then the probability of belonging to any class is ambiguous [13].…”

Sparse Kernel Machines for motor imagery EEG classification

“…One major challenge in MI BCI is the real-time extraction of reliable information from noisy data in the form of relevant features. The existing feature extraction approaches are dominated by methods estimating the distribution of energy in various domains, such as the time domain, the frequency domain, the time -frequency (t-f) domain, the wavelet domain and the spatial domain [3]- [9]. One of the most popular and efficient algorithms for MI BCI relies on the use of Common Spatial Patterns (CSP).…”