Fuzzy clustering-based feature extraction method for mental task classification

Abstract

A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property. By adopting the virtue of both techniques, a theoretical adaptive filter-based method to decompose non-linear and non-stationary signal has been proposed known as empirical wavelet transform (EWT) in recent past. EWT does not work well for the signals having overlapped in frequency and time domain and failed to provide good features for further classification. In this work, Fuzzy c-means algorithm is utilized along with EWT to handle this problem. It has been observed from the experimental results that EWT along with fuzzy clustering outperforms in comparison to EWT for the EEG-based response to mental task problem. Further, in case of mental task classification, the ratio of samples to features is very small. To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz. Bhattacharyya distance (BD), ratio of scatter matrices (SR), and linear regression (LR). The results of experiment demonstrate that the performance of mental task classification has improved considerably by aforesaid methods. Ranking method and Friedman's statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.

Keywords: Brain computer interface; Empirical wavelet transform; Feature extraction; Feature selection; Fuzzy C-means clustering; Mental tasks classification.

Fig. 1

Electrode placement of EEG recording adapted from [13]

Fig. 2

Eight features obtained for different tasks for channel 1 from segment 1 using FEWT for subject-1

Fig. 3

Performance of SVC for subject-1

Fig. 4

Performance of SVC for subject-2

Fig. 5

Performance of SVC for subject-3

Fig. 6

Performance of SVC for subject-5

Fig. 7

Performance of SVC for subject-6

Fig. 8

Performance of SVC for subject-7

Fig. 9

Ranking of combinations of feature selection methods with FEWT extraction method