Research Article Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function Md. Mostafizur Rahman and Shaikh Anowarul Fattah Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh Correspondence should be addressed to Shaikh Anowarul Fattah; fattah@eee.buet.ac.bd Received 8 June 2017; Accepted 10 September 2017; Published 10 December 2017 Academic Editor: Yudong Cai Copyright © 2017 Md. Mostafzur Rahman and Shaikh Anowarul Fattah. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In view of recent increase of brain computer interface (BCI) based applications, the importance of efcient classifcation of various mental tasks has increased prodigiously nowadays. In order to obtain efective classifcation, efcient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from diferent combination of channels will be diferent for diferent mental tasks, which can be exploited to extract distinctive feature. Te empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefcient is extracted from interchannel IMF data. Simultaneously, diferent statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Diferent kernels of the support vector machine (SVM) classifer are used to carry out the classifcation task. An EEG dataset containing ten diferent combinations of fve diferent mental tasks is utilized to demonstrate the classifcation performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods. 1. Introduction Electroencephalogram (EEG) signal is used extensively nowadays by the researchers to handle diferent applications of brain-computer interface (BCI). EEG-based BCI systems employ electrical activity of brain to classify diferent EEG signals corresponding to various mental tasks precisely. One way to classify the signals efectively is to acquire discriminative features from that signal. As a matter of fact, diferent schemes to extract distinctive features are available in literature. For example, in [1], spectral power and asymmetry ratio based feature extraction scheme is proposed where an additional band (24–37 Hz) is used along with con- ventional lower spectral bands for mental task classifcation. Tis method ofers comparatively satisfactory classifcation performance but lacks consistency for all cases. In [2], similar feature extraction scheme used in [1] is proposed; however, the diference is that it utilizes an additional high frequency band (40–100 Hz) to obtain those features. In [3], a dictionary consisting of power spectral density and common spatial pat- tern (CSP) algorithm is introduced to classify various mental tasks. Autoregressive (AR) model based feature extraction scheme is reported in [4] where sixth-order AR system is considered to extract feature. Moreover, in [5], multivariate AR models are taken into consideration and four diferent representations of AR coefcients are tested to classify mental task. In [6], feature extraction scheme based on sparse autoregressive model is investigated, which involves complex computation to exclude autoregressive coefcients that are useless in the prediction stage. In [7], a feature extraction method based on generalized Higuchi fractal dimension spectrum along with AR parameters is proposed. Wavelet transform and empirical mode decomposition (EMD) based classifcation methods are proposed in [8], where feature selection method is utilized for better classifcation per- formance. In [9], Stockwell transform based algorithm is Hindawi BioMed Research International Volume 2017, Article ID 3720589, 11 pages https://doi.org/10.1155/2017/3720589