Decoding SteadyState Visual Evoked Potentials From EEG Signals: Towards an EEGTriggered FES System to Restore Hand Grasp Function It has been widely reported that patterns of EEG generated when a person performs a mental strategy can be recognized by signal processing algorithms. Among those mental strategies are the EEGbased braincomputer interface (BCI) paradigms. Furthermore, recognized patterns can be used as a source of information for communication to operate devices of BCI. SteadyState Visually Evoked Potentials (SSVEP) is a BCI paradigm that uses EEG brain responses when a subject focuses on a visual stimuli (flickering stimuli). Decoding SSVEP signals refers to identify what stimulus the user focuses on, which could be used as a command for communication or control. The minimum energy combination (MEC) and canonical correlation analysis methods (CCA) have been used in SSVEPbased BCIs due to its high efficiency, robustness, and simple implementation. In the last years, variants of CCAbased SSVEP methods have been reported in literature to improve classification and usability such as filter bank canonical correlation analysis (FBCCA). This paper evaluates the MEC, CCA and FBCCA methods for decoding commands from EEG signals in a SSVEPbased BCI application. It was carried out a set of experiments with five subjects which consist of four flickering stimuli (6.66, 7.5, 8.57 and 10 Hz) showed on a LED monitor. The results showed, for an epoch of 3 s, that CCA and FBCCA methods were able to detect SSVEP with high accuracy: 92.6% for FBCCA and 91.4% for CCA. The classification accuracy was 86.1% for MEC. As future work, FBCCA method will be used to decode user intention to control a closedloop system based on EEGtriggered FES to restore hand grasp function.

Study on Novel Designs with Reduced Fatigue for Steady State Motion Visual Evoked Potentials The paper focuses on incorporation of Brain Computer Interfacing (BCI) within an Augmented Reality (AR) platform to provide means for individuals with communication disabilities to interact with the outer world. Recently, there has been a recent surge of interest on SteadyState Visual Evoked Potentials (SSVEP). In a typical SSVEPbased BCI system, the virtual object within the AR environment flickers with a specific frequency while the signal processing module extracts the effects of the flickering frequency on the Electrophysiological (EEG) signals. Despite the popularity of SSVEPs, their utilization for practical application especially for assistive technologies is complicated and challenging due to eye fatigue and risk of induced epileptic seizure. In this regard, the key issue being targeted in this paper is addressing fatigue of flicker (or brightness modulation) by development of flickerfree steadystate motion visual evoked potential (SSMVEP). Two novel SSMVEP paradigms, i.e., Squarebased and Circlebased paradigms, with low luminance contrast and oscillating expansion and contraction motions are designed, and integrated within a BCI system. Through experimental evaluations, high detection accuracy of 95.31% is achieved for the squarebased SSMVEP.