An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

Abstract

Introduction A brain-computer interface (BCI) system enables nerve and muscle free interaction of human with surrounding.1 Some recent and important research applications of BCI are human to human interface, neuroprosthetics, exoskeleton control, mobile and guided robotics, biometrics, neurogaming and Intelligent Transportation. [2][3][4][5] Due to the wide range of applications, BCI systems have the potential to use by both normal and disabled individual.6 Using this technology, severely disabled, paralyzed or who have neuromuscular diseases such as amyotrophic lateral sclerosis, stroke or spinal cord injuries become self-sufficient in fulfilling their basic requirements and severe motor disabilities. 7,8 Actually, BCI systems increase the quality of their life while reduce the burden and cost of care. Came the request in mind, creates a unique brain signal which is recognizable for an intelligent computational system. 10,11 Various techniques have been developed to extract brain signals which include magneto electroencephalography (MEG), functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS), electrocorticography (ECoG) and electroencephalography (EEG).2 EEG has advantages over other techniques, the most important of which is its good temporal resolution.12 Systems for recording EEG signal are also non-invasive, inexpensive, free of any radiation, and can be simply implemented.2 Thus, in order to capture motor imaginary brain activities in a BCI system, the EEG signal is commonly used.13 An EEG-based BCI system uses EEG as the control signal in neural and muscular free interaction of human with surrounding.1 In different parts of the EEG-based BCI system, the user's EEG signals captured by the electrodes and to decode and recognize the intended interactions are sent to the processor. 14The main purpose of EEG-based BCI is to translate EEG signal into a command for a computer.14 Generally, the design of such a system is very complex. 9 In many researches that have been done so far on EEG-based BCI systems, actually the system is reduced to a classifier

Identifying motor imagery activities in brain computer interfaces based on the intelligent selection of most informative timeframe

“…Therefore an optimal set of the most distinctive features should be selected for each subject. As the search space for such a problem is extensive, a systematic data mining method [35] doesn't seem to be a comprehensive solution. Indeed, an intelligent optimization technique should be provided so that it can automatically find both the optimum timeframe across the EEG signal as well as the optimal set of features within the timeframe.…”