24 NNGT Journal: International Journal of Information Systems Volume 1 July 30,2014 Exploiting a Short-Terms Adaptation: In Brain Computer Interface Based on Steady State Visual Evoked Potential Mustafa Aljshamee, Mahdi Q. Mohammed Department of Computer Science University of Rostock Rostock, Germany mustafa.aljshamee@uni-rostock.de mahdi.mohammed@uni-rostock.de Abbas Malekpour * , Peter Luksch * Department of Computer Science University of Rostock Rostock, Germany abbas.malekpour@uni-rostock.de peter.luksch@uni-rostock.de Abstract— The emergence of successful Brain Computer Interfaces (BCI) that can assist the healthy and aid the disabled users, has introduced new limitless possibilities for wider use of brain technology in society. And now, existing BCI designs are still unable to fulfill such high expectations due to restricted reliability, insufficient usability, and inadequate understanding of the underlying brain mechanisms. This work aimed to address directly the above problems by presenting and optimizing a new multi- command BCI designs based on BCI. Where a novel high-response and high-amplitude of SSVEP based on BCI, which lead to distinguishable brain response, was implemented a new SSVEP paradigms, in which to induced irregular flickering respect to phase-tagged en route of brain activity, which effectively mean, creating a multiple-frequencies combination, were considered a closed-loop system to customized control a stimulation flicker panel, notwithstanding as off-line analysis, where the combining average of stimuli frequency is recommended using Fast Fourier Transform (FFT) and Event Related Potential (ERP). However the distinguishable the brain responded in terms of intention detection by extracted the feature of electroencephalograms (EEG) and frequencies, in this experimental study, different results between regular versus irregular had been found, represented by high- amplitude with respect to conjunctive of stimuli flicker according to a patterns and frequencies. Keywords- WSWAN’2014; Brain Computer Interface (BCI); electroencephalograms (EEG); steady state visual evoked potential (SSVEP); Fast Fourier Transform (FFT); Event Related Potential (ERP); Field Programmable Gate Array (FPGA). Introduction A patient suffering from severe motor disabilities, such as amyotrophic lateral scleroses (ALS), spinocerebellar ataxia (SCA) and other paralyzed patient may have limited motion space [1]. This limitation inspired the scientists to find another manner for communication. The Eye tracking is one of these methods and suggested to help disabled people to control their facilities life [2] [3]. The Brain Computer Interface (BCI) is a direct communication channel [4] between the brain and the outside world, by extracting EEG features with high speed of information transfer rate, the identifying brain’s commands, and transfer them to external- controlled instruments to provide more comfortable environment for disabled people. The most of experimental studies of brain activity recorded from the head scalp are concerned either analysis of difference in the mean of EEG frequencies spectral under various conditions, or with measure on average of precisely time-lock brain response activity by evoked a discrete experiment stimuli or flickering event. Currently, EEG signals, such as P300 evoked potentials [5], visual evoked potential (VEP) [6], mu rhythms [7], and slow cortical potentials (SCP) [8] are commonly used as the input signal for BCI systems. The Steady-state visual evoked potential (SSVEP) is an EEG signal response to the flickering visual stimulus as a large range of frequencies, from 1 to 90 Hz [1], but good response are normally acquired between 5 to 27 Hz [9]. The frequency coding approach has been widely used in SSVEP-based on BCI systems with personal computer (PC) instruments [10] and implements a frequency-coded SSVEP based BCI. Another application shown in [11] is frequency- coded SSVEP based system for controlling the two-axis electrical hand prosthesis with four LED stimulators mounted on the prosthesis. The EEG signals response show the same frequency as the flickering stimulus [12]. An SSVEP for BCIs has advantages such as short response time, minimal training requirements, and the records of EEG signals from the occipital area as input for communication or device control [13] [14] [15] [16]. Most of SSVEPs use only a single-frequency flicker for each selection of flash stimulators [13] [14] [16]. SSVEP-based BCI system using the phase coding flashing light technique [15], which is different phase sequences at the same flickering frequency drive different flickering visual stimuli. The motivation of experimental thesis studies as practical BCI system, in addition to propose a design system of a BCI controller. The phase sequences used to drive * Corresponding authors, both authors are equally contribution