In recent years, there has been an increase in the usage of consumer based EEG devices with fewer channel configuration. Although independent component analysis has been a popular approach for eye-blink artifact removal from multichannel EEG signals, several studies showed that there is a leak of neural information into the eye-blink artifact associated independent components (ICs). Furthermore, the leak increases as the number of input EEG channels decreases and leads to loss of valuable EEG information. To overcome this problem, we developed a new framework that combines ICA with continuous wavelet transform (CWT), k- means and singular spectrum analysis (SSA) methods. In contrast to the existing approaches, the artifact region in the identified eye-blink artifact IC is detected and suppressed rather than setting it to zero as in classical ICA. As most of the energy in the eye-blink artifact IC is concentrated in the artifact region, CWT and k- means algorithms exploits this feature to detect the eye-blink artifact region. Support vector machine (SVM) based classifier is finally designed for automatic detection of the eye blink artifact ICs. The performance of proposed method is evaluated on synthetic and two real EEG datasets for various EEG channels setting. Results highlight that for fewer channel EEG signals, the proposed method provides accurate separation without any neural information loss as compared to the existing methods.