Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface.

Abstract

Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigueduring prolonged BCI use (Lorenz et al 2014 J. Neural Eng. 11 035007). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% ( ± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 ( ± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are difficult to satisfy.