Simultaneous Detection of P300 and Steady-State Visually Evoked Potentials for Hybrid Brain-Computer Interface

Abstract

ObjectiveWe study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI.ApproachWe analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline.Main ResultsDespite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

“…To find the best parameters for each user, an offline training stage is performed to test 11 flickering frequencies (20 Hz,20. Figure 4 shows the spectrum of the 3-s EEG signal recorded at Oz, respectively, where the flickering frequency of the LED is 24.39 Hz. We can observe from this spectrum the peaks at its first (24.39 Hz) and the second (48.78 Hz) harmonic frequencies.…”

“…Finally, the target item in this matrix is estimated by using a maximum-probability estimation (MPE) method to fuse the scores obtained from the RC P300 and the two-step SSVEP paradigms. Therefore, the hybrid BCI of [35] utilizes the P300 and SSVEP fusion method to gain better target item detection accuracy than a pure P300-based BCI, which is the same as the objective of the hybrid BCI in [20] where both P300 and SSVEP paradigms are performed simultaneously to get better detection accuracy than a pure P300-based BCI. Therefore, the SSVEP and P300-based BCIs proposed in [20,35] are based on a parallel architecture, which is different from the proposed BCI.…”

“…SSVEP and P300 are visual evoked potential and event related potential (ERP) in EEG, respectively [20,21]. SSVEP is a response to visual stimuli at specific flicker frequencies, where the response is the increase of EEG spectral power at the same frequency or near the flickering frequency [22].…”

“…However, EEG typically exhibits a very low SNR [26]. Therefore, P300-based BCIs rely on multiple repetitions of a stimulation sequence in order to increase the SNR of the P300 ERP [20], which results in a long EEG recording procedure.…”

“…LDA has also been used for dimensionality reduction in various pattern recognition problems, such as face recognition [32,33] and electromyography (EMG) classification [34]. In the BCI community, LDA is often employed as a classifier [20,26,[35][36][37]. Recently, LDA has also been introduced to EEG-based emotion recognition as a dimension reduction method [17].…”