An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Abstract

a b s t r a c tFeature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain-computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance.

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

“…Thus, refinement of the algorithm was necessary. The original spMCA [39] was iterated several times (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) with the core input data shuffled and appended with the data points representing means of clusters found in the previous iteration. For each iteration the record of a cluster validity index (described below) was kept.…”

Review of Recent Type-2 Fuzzy Image Processing Applications

“…Papers reviewed in this section [10,50,53] use real images, where [50,53] are focused on medical applications. They use different metrics to evaluate the classification accuracy in which we can single out the Friedman test, contingency coefficient, and Kappa.…”

“…They use different metrics to evaluate the classification accuracy in which we can single out the Friedman test, contingency coefficient, and Kappa. In [10,50], the training set and testing set are obtained using the cross-validation technique. All papers show comparative results against T1 FS as well as other classifier techniques, where, in accordance with the classification rates, the approach based on T2 FS [50,53] is better than T1 FS approaches and other known methods.…”

“…In [10,50], the training set and testing set are obtained using the cross-validation technique. All papers show comparative results against T1 FS as well as other classifier techniques, where, in accordance with the classification rates, the approach based on T2 FS [50,53] is better than T1 FS approaches and other known methods. Based on these results, we can observe that T2 fuzzy techniques perform better when applied to classification applications, especially when digital images are incomplete or involve some noise, in which case it is difficult for the system to determine if a pixel is part or not of a class.…”

“…In Saugat Bhattacharyya et al (2015) [50], an IT2 fuzzy approach for real-time EEG-based control of wrist and finger movement was proposed. The classification of motor imaginary signals for wrist rotation and grasping activity were performed using an IT2 FS classifier.…”