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

Abstract

Abstract-One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain-computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data generating mechanism. The objective of this work is thus to examine the applicability of T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: i) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery (MI), and ii) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis (LDA), kernel Fisher discriminant (KFD) and support vector machines (SVMs) as well as a conventional type-1 FLS (T1FLS), simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification. commonly exploited soft computing paradigms in pattern recognition [1]. More specifically, fuzzy methodology has enjoyed considerable popularity in the problem domains where multifarious manifestations of uncertainty are particularly difficult to capture analytically. Brain signal pattern recognition, which lies within the focus of this work, serves as the prime instance in this regard. Consequently, the literature reports the abundance of FL based approaches to analysis, interpretation and classification of brain neurophysiologic data, especially electroencephalogram (EEG), e.g. [2]- [7]. This paper addresses a particularly challenging task of discriminating patterns in the EEG signals recorded from subjects performing motor imagery (MI), which has already received some attention in fuzzy pattern recognition [3], [4] [8]- [11]. The outcome of these investigations has intrinsic implications for the broader field of EEG-based brain-computer interface (BCI). The significance of BCI technology is reflected in its contribution to the improvement of living standards for people affected from neuromuscular disorders [12], [13] and, more recently, in its potential to support a range of rehabilitative therapies [14], [15] as well as the growing impact on computer games industry [16]. One of the key challenges in the field of...

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“…10 and 11. It is confirmed from both the figures that alpha band (8-13 Hz) is associated during visual alertness and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) band is active during motor execution tasks.…”

“…The acquired EEGs from pre-frontal/frontal, parietal and motor cortex regions are pre-processed using band pass filters (BPFs) of suitable frequency bands. VA being more prominent in alpha band (~8-13 Hz) [41] and MP/ME being relatively more active in mu-(8-13 Hz) [42] and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [43] bands, we used BPFs of required pass bands. More review on EEG channel selection and frequency band selection are provided in [44], [45].…”

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