It has become a challenging research topic to design and develop cheap and wearable brain-computer interface (BCI) systems but not compromising the performance. In this thesis, the design and development of a steady state visually evoked potential (SSVEP) based BCI system has been presented which is a low cost, wearable BCI system and gives highly accurate target identi cations with good information transfer rate (ITR). It is a battery powered, wireless BCI system and ensures the complete isolation to the subject. Like all the BCI systems, it is designed and implemented in ve major parts: (i) stimulator which is a microcontroller based circuit and provides the frequency modulated visually evoked potential (f-VEP) and code-modulated visually evoked potential (c-VEP) stimulations (ii) dry active electrodes which capture the electroencephalography(EEG) signals from the O1, O2, and Oz head positions (iii) high sampling rate, 4-channel EEG data acquisition hardware which acquires the EEG signals, amplify them, converts them to digital data, and transmits the data using wi communication (iv) the data processing unit (DPU) which is a MATLAB script to process the raw EEG data and displays the results and (v) the headset which mounts all the components except DPU and is developed using 3D printing technology. The rst prototype of the proposed BCI system has been developed in 331 USD and tested for both the f-VEP and c-VEP modalities on six human subjects. For f-VEP modality, it exhibits an average accuracy (live accuracy) of 92.1% and average ITR (live ITR) of 69.5 bits/min on the basis of target identi cations done on 1:04 s data recordings. If we extract one message character from ve consecutive target identi cations, the average message accuracy goes to 98.8% and average message ITR to 17.2 bits/min. In case of c-VEP modality, it exhibits live accuracy of 70.1 % and live ITR of 23.5 bits/min while message accuracy of 90.7 % and message ITR of 12.4 bit/min.