Abstract

This study evaluated the effect of change in background on steady state visually evoked potentials (SSVEP) and steady state motion visually evoked potentials (SSMVEP) based brain computer interfaces (BCI) in a small-profile augmented reality (AR) headset. A four target SSVEP and SSMVEP BCI was implemented using the Cognixion AR headset prototype. An active (AB) and a non-active background (NB) were evaluated. The signal characteristics and classification performance of the two BCI paradigms were studied. Offline analysis was performed using canonical correlation analysis (CCA) and complex-spectrum based convolutional neural network (C-CNN). Finally, the asynchronous pseudo-online performance of the SSMVEP BCI was evaluated. Signal analysis revealed that the SSMVEP stimulus was more robust to change in background compared to SSVEP stimulus in AR. The decoding performance revealed that the C-CNN method outperformed CCA for both stimulus types and NB background, in agreement with results in the literature. The average offline accuracies for W = 1 s of C-CNN were (NB vs. AB): SSVEP: 82% ±15% vs. 60% ±21% and SSMVEP: 71.4% ± 22% vs. 63.5% ± 18%. Additionally, for W = 2 s, the AR-SSMVEP BCI with the C-CNN method was 83.3% ± 27% (NB) and 74.1% ±22% (AB). The results suggest that with the C-CNN method, the AR-SSMVEP BCI is both robust to change in background conditions and provides high decoding accuracy compared to the AR-SSVEP BCI. This study presents novel results that highlight the robustness and practical application of SSMVEP BCIs developed with a low-cost AR headset.

Highlights

  • Eelectroencephalography based brain computer interfaces (BCIs) enable humans to establish a direct communication pathway between the brain and the external environment bypassing the peripheral nerves and muscles [1]

  • For both state visually evoked potentials (SSVEP) and steadystate motion visually evoked potentials (SSMVEP) stimulus, the spectral analysis revealed the presence of a prominent peak at the fundamental frequency and its harmonics

  • optical see-through (OST) system is a good candidate for the implementation of SSVEP/SSMVEP BCIs in augmented reality (AR)

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Summary

Introduction

Eelectroencephalography based brain computer interfaces (BCIs) enable humans to establish a direct communication pathway between the brain and the external environment bypassing the peripheral nerves and muscles [1]. The BCI can determine which stimulus occupies the user’s visual attention by detecting the SSVEP response at the targeted stimulus frequency from the EEG recorded at the occipital and parieto-occipital cortex This will appear as a significant peak at the targeted stimulus frequency and potentially at its higher order harmonics [2], [3]. The SSVEP stimuli are most commonly designed as a monochromatic object whose intensity is modulated at a fixed frequency, as a result, it appears as a flashing object to the user. This flashing stimulus can induce visual fatigue and discomfort. SSMVEP BCIs share the advantages of SSVEP BCI such as high SNR, high information transfer rate (ITR) and low participant training time compared to other types of BCIs [6], [7] while minimizing SSVEP-related discomfort for operators

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