## 2 Related Work  
A hybrid method employing common spatial pattern filter has been used by [PERSON] et al. [5] for extracting the features. Different optimisation techniques, like Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Artificial Bee Colony (ABC), and Simulated Annealing. These methods have been used along with support vector machine (SVM) classifier; however, the performance is slower when compared to the conventional techniques. Dimensionality reduction techniques have been investigated by [PERSON] et al. [6] by using the Hjorth parameters and adaptive autoregressive coefficients. The feature selection techniques that were used were sequential forward selection and sequential backward selection, while the classification techniques used were Principal Component Analysis (PCA), Local Fisher Discriminant Analysis (LFDA), and Locality Preserving Projections. The performance of the classification process has been increased using these hybrid techniques. However, the number of channels and subjects used are very less and, hence, more detailed analysis is necessary. Wavelet transform was used by [PERSON] et al. [7] for extracting the features. Six types of filter methods, which are Euclidean distance (ED), Bhattacharyya distance measure (BD), Kullback-Leibler distance (KD), ratio of scatter matrices (SR), linear regression (LR), and maximum relevance minimum redundancy (mRMR), have been used for decreasing the size of feature vectors. Each of the six feature selection techniques has increased the performance of the classification. From these techniques, the combination of wavelet transform and linear regression is seen to have the best performance. A feature reduction algorithm has been proposed by [PERSON] et al. [8] for the EEG framework. Pre-processing is performed while using autoregressive coefficients. The three-dimensional EEG signals are converted to two-dimensional matrix by compressing the feature vectors. Different techniques have been used for the ranking process, such as RFS, SSLSR, and RUFS. However, only two motor imagery tasks have been performed in this work. [PERSON] and [PERSON] [9] has been classified the Motor Imagery while using combinations of feature extraction and dimensionality reduction approaches. FFT, TDP, band power, and channel variance are the methods used by the researchers. The obtained values are combined together in pairs. Different feature reduction techniques have been analysed, such as PCA, sequential selections, LPP, and LFDA. It has been observed that the combined techniques have higher accuracy than the individual techniques. Hence, it compares the various combination of techniques for improved accuracy and recommends the FFT, CV, and PCA methods along with the LS-SVM classification. Hence, this work will combine the different techniques for improving the accuracy. [PERSON] and [PERSON] [10] proposed a method for finding the subject specific time intervals for the classification of four-class motor imagery tasks by using mutual information (MI) between the BCI input and output. The signal-to-noise ratio was utilized to compute the MI values while the MI values were utilized as feature selection criteria to select the discriminative features. The time segments and the better discriminative features were found by using training data and used to estimate the evaluation data. The filter bank common spatial pattern (FBCSP) algorithm has been divided in to four progressive stages, such as filter-bank, the CSP algorithm, feature selection, and classification. However, the noise and artifacts of the EEG signals have been unnoticed in the experiment. [PERSON] et al. proposed a feature extraction technique that has combined the feature extraction and feature selection method [11]. Four different feature extraction methods were used to reduce the number of features from a total of 83 features. Three different feature selection techniques were used and then compared. The Fisher score has been seen to have the most accurate results.