An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement. (August 2015)

• Record Type: Journal Article
• Authors: Bhattacharyya, Saugat; Pal, Monalisa; Konar, Amit; Tibarewala, D.N.
• Is Part Of: Biomedical signal processing and control. Volume 21(2015)
• Page Start: 90
• Page End: 98
• Publication Date: 2015-08
• Subjects: Electroencephalography -- Interval type-2 fuzzy systems -- Motor imagination -- Extreme energy ratio -- Wrist movement and grasping; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.05.004
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Session-to-session EEG variation is addressed and tackled by the proposed method. Interval type-2 fuzzy classification approach adopted for the purpose. Studied on discrimination of wrist and fingers motor imagery EEG signals. Offline and online experiments establish the efficacy of the proposed method. Using Extreme Energy Ratio as features 86.45% and 78.44% accuracies are obtained. Abstract: Feature 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.