Session Program


  • 10 July 2017
  • 02:00PM - 04:00PM
  • Room: Santa Lucia
  • Chairs: Javier Andreu-Perez and CT Lin

Fuzzy Logic Systems for Brain Analysis and Brain- Computer Interface

Abstract - In this paper, a novel technique for the computation of effective brain connectivity in functional Near-Infrared Spectroscopy (fNIRS) data is presented. The estimation of effective brain connectivity using the proposed approach of higher order Fuzzy Cognitive Maps (FCMs), used in conjunction with Genetic Algorithm (GA), is shown to be more accurate. Owing to lack of dependency on human knowledge, the FCM-GA model becomes more robust to subjective beliefs of experts from various domains when establishing connectivity matrix. Furthermore, higher order FCMs are capable of assessing causal relations in historical data with variable time lag, g, therefore generating more accurate predictions for complex causal data such as fNIRS where the causality may not necessarily follow a first order dynamics. The computation model of higher order FCM-GA is shown to perform better than Granger Causality (GC) for estimating effective brain connectivity in synthetic fNIRS data at 95\% significance level. The proposed approach is also tested on real fNIRS data, and shown to estimate the causal structure amongst region of interests (ROIs) with improved accuracy.
Abstract - Adaptive neuro fuzzy inference systems (ANFIS) has been applied in brain computer interfaces (BCI) in different ways, mapping P300 to known wave or fusing information from EEG channels; these applications have obtained high accuracies greater than 85\%. This work proposes an ANFIS ensemble classifier, where it decide its output by voting to detect P300 wave in a BCI that could be used for smart home interaction; using four channels in the positions P4, P7, O1, and O2. Five healthy subjects and three post-stroke patients have participated in this study, each participant performs 4 BCI sessions, and crossvalidation is applied to evaluate the classifier performance by session. The results of average accuracy were greater than 75\% for all subjects, similar results were gotten for healthy subjects a post-stroke patients, but the better ANFIS ensemble classifiers for each subject have gotten accuracies greater than 80\%. These results show the suitability of proposed classifier in this interactive application.
Abstract - The paper attempts to model human working memory using fuzzy relational equation with an aim to retrieve the relevant stored information in the memory from the partial input using the model. Psycho-physiological experiments have been developed to validate the model to match the model generated memory-response with the actual memory response using EEG signals acquired during memory encoding and recall phases. The fuzzy relational equation developed here represents brain connectivity in the fuzzy space between the encoding and the recall instances. The paper introduces a novel approach to compute inverse fuzzy relation with respect to max-min composition operator to determine the short-term memory information from the working memory, when the latter is stimulated with partial faces of people already encoded in the short-term memory. An error metric is defined to measure the error amplitude between the model-predicted encoding pattern and the actual pattern encoded in the short-term memory. A small value in error indicates a good accuracy of the proposed working memory model, and thus can be used to discriminate people with brain-related diseases. Experiments undertaken reveal that the error metric could be used successfully to detect memory failures in five patients, two of which suffer from Parkinson, two from the early Alzheimer's disease and one from frontal lobe damage.
Abstract - This work presents a brain computer interface (BCI) framework for upper limb rehabilitation of post stroke patients, combining BCI and virtual reality (VR) technology; a VR feedback is shown to the participants to achieve a greater activation of certain brain regions involved with the performing of upper limb motor task. This system uses an adaptive neuro-fuzzy inference system (ANFIS) classifier to discriminate between a motor task and rest condition, the first one classifies between extension and rest conditions; and the second one classifies between flexion and rest conditions. In the training stage, eight healthy subjects participated in the sessions, the best accuracies are 99.3\% and 88.9\%, as a result of cross-validation. Meanwhile, the best accuracy in online test is 89\%. The methodology here presented can be straightforwardly employed as a rehabilitation system for brain repair in individuals with neurological diseases or brain injury.
Abstract - This study considers the dynamic changes of complexity feature by fuzzy entropy measurement and repetitive steady-state visual evoked potential (SSVEP) stimulus. Since brain complexity reflects the ability of the brain to adapt to changing situations, we suppose such adaptation is closely related to the habituation, a form of learning in which an organism decreases or increases to respond to a stimulus after repeated presentations. By a wearable electroencephalograph (EEG) with Fpz and Oz electrodes, EEG signals were collected from 20 healthy participants in one resting and five-times 15 Hz SSVEP sessions. Moreover, EEG complexity feature was extracted by multi-scale Inherent Fuzzy Entropy (IFE) algorithm, and relative complexity (RC) was defined the difference between resting and SSVEP. Our results showed the enhanced frontal and occipital RC was accompanied with increased stimulus times. Compared with the 1st SSVEP session, the RC was significantly higher than the 5th SSVEP session at frontal and occipital areas (p $<$ 0.05). It suggested that brain has adapted to changes in stimulus influence, and possibly connected with the habituation. In conclusion, effective evaluation of IFE has a potential EEG signature of complexity in the SSEVP-based experiment.
Abstract - This paper presents an approach to analysis of multiclass EEG data obtained from the brain computer interface (BCI) applications. The proposed approach comprises two stages including feature extraction using the common spatial pattern (CSP) and classification using fuzzy logic systems (FLS). CSP is used to extract significant features that are then fed into FLS as inputs for classification. The metaheuristic population-based particle swarm optimization method is used to train parameters of the FLS. The multiclass motor imagery dataset IIa from the BCI competition IV is used for experiments to highlight the superiority of the proposed approach against competing methods, which include linear discriminant analysis, naive bayes, k-nearest neighbour, ensemble learning AdaBoost and support vector machine. Results from experiments show the great accuracy of the combination of CSP and FLS. Therefore, the proposed approach can be implemented effectively in the practical BCI systems, which would be helpful for people with impairments and rehabilitation.