Classification of left and right foot kinaesthetic motor imagery using common spatial pattern

Brain-computer interface (BCI) systems typically deploy common spatial pattern (CSP) for feature extraction of mu and beta rhythms based on upper limbs kinaesthetic motor imageries (KMI). However, it was not used to classify the left vs. right foot KMI, due to its location inside the mesial wall of the sensorimotor cortex, which makes it difficult to be detected. We report the novel classification of mu and beta EEG features, during left and right foot KMI cognitive task, using CSP, and the filter bank common spatial pattern (FBCSP) method, to optimize the subject-specific band selection. We initially proposed CSP method, followed by the implementation of FBCSP for optimization of individual spatial patterns, wherein a set of CSP filters was learned, for each of the time/frequency filters in a supervised way. This was followed by the log-variance feature extraction and concatenation of all features (over all chosen spectral-filters). Subsequently, supervised machine learning was implemented, i.e. logistic regression (Logreg) and linear discriminant analysis (LDA), in order to compare the respective foot KMI classification rates. Training and testing data, used in the model, was validated using 10-fold cross validation. In this study four methodology paradigms were reported, i.e. CSP LDA, CSP Logreg, and FBCSP LDA, FBCSP Logreg. All paradigms resulted in an average classification accuracy rate above the statistical chance level of 60.0% (P < 0.01). On average, FBCSP- LDA outperformed remaining paradigms with a kappa score of 0.41 and the area under ROC curve as 0.64. Similarly, this paradigm enabled discrimination between right and left foot KMI cognitive task at highest accuracy rate i.e. maximum 77.5% with kappa=0.55 (in single-trial analysis). The proposed novel paradigms, using CSP and FBCSP, established a potential to exploit the left vs. right foot imagery classification, in synchronous 2-class BCI for controlling robotic foot, or foot neuroprosthesis.