## 1 Introduction  
The possibility of controlling other devices while using brain functions has become feasible. The brain signals can be intercepted through the process of Brain Computer Interface (BCI). This is possible by recording the various signals from the brain and analysing them to identify the type of the signal based on the time and frequency of the signal [1]. The signals can be internally and externally collected from the brain. It is more effective in collecting these signals from internal measurement, since the measured values are more accurate. The externally measured signals are of comparatively lower quality when compared to the internally measured values [2]. However, internal measurements are a potentially dangerous technique, since the measuring devices have to be surgically placed inside the body, leading to higher chances of life threat and, hence, it is also known as an invasive method. The externally measured non-invasive method does not require any form of surgical procedure and is more preferred in-spite of its comparatively lower quality. Some of these non-techniques are functional Magnetic Resonance Imaging (fMRI), Computed Tomography (CT), Magneto-Encephalo-Graphy (MEG), and Electro-Encephalo-Graphy (EEG). When these techniques are compared, EEG is a better technique, since it is easier to use, lower investment cost, and has a comparatively superior temporal resolution [3; 4]. Hence, it is the most popular BCI measurement technique. A measuring device is placed on the scalp, where it detects the electrical neural signals and identifies the type of patterns, like the state of the subject. It identifies whether the subject is calm, hypnotised, or angry. Identifying the state of the subject from the signal is challenging and, hence, the classification must be done appropriately by initially training the algorithm efficiently. The BCI has lots of advantages and it has the capability of using it in a variety of applications. This interface can be used by the physically challenged for controlling the directions of the wheelchair. Another application is where the computer input functions, like the mouse pointer or the cursor of the computer, can be controlled. Motor Imagery is the process where these simulations are performed while using brain. The appropriate frequency bands that present these features are Beta band and Mu band in the EEG systems. These frequency bands that include the important features vary according to the individual and also depend upon the event dependent property. Hence, it is necessary to analyse the individual EEG signals and with respect to the time domain and frequency domain, so that the characteristics can be learnt. There are lots of feature extraction techniques and classification techniques available that have been performed by various researchers. However, the accuracy of the approaches is not up to the mark and, hence, the novel techniques must be identified for increasing the accuracy. This paper has compared different feature extraction techniques and classification techniques by combining them in various combinations in order to identify the most efficient combination. Our contribution of this work is combining user specific band and subject specific band and two feature extraction methods together. In this research, we studied different time and frequency intervals for each subject and determined the best subject specific time and intervals for the classification of four-class motor imagery tasks. Different time intervals were examined with various feature extraction methods and classifiers to find out the best time intervals each subject who performed motor imagery. User specific and subject specific bands are both effective in performance improvement. This paper aims to show using combination of user specific and subject specific bands would have better performance than just using user specific and subject specific bands alone. Our proposed method yielded better classification performances than the state-of-the-art methods.