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. 2023 Feb 12;23(4):2069.
doi: 10.3390/s23042069.

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

Nannaphat Siribunyaphat  1 , Yunyong Punsawad  1   2
Affiliations

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

Nannaphat Siribunyaphat et al. Sensors (Basel). .

Abstract

Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

Keywords: QR code; brain–computer interface; quick response; steady-state visual evoked potential; wheelchair.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed SSVEP-based BCI system using QR code visual stimulus pattern for simulated wheelchair control.
Figure 2
Figure 2
SSVEP stimulus pattern (size: 3 cm × 4 cm). (a) QR code pattern; (b) Checkerboard pattern.
Figure 3
Figure 3
EPOC FLEXTM device and accessories (https://www.emotiv.com, accessed on 8 September 2022).
Figure 4
Figure 4
Components of the EEG acquisition using an EMOTIV EPOC Flex for a real-time BCI system.
Figure 5
Figure 5
Screenshot of visual stimuli with four fundamental flicker frequencies of four BCI commands (Table 2 and Table 3) to control the simulated wheelchair through an LCD monitor. (a) QR code pattern (proposed); (b) Checkerboard pattern (traditional).
Figure 6
Figure 6
Average classification accuracy between absolute PSD and relative PSD power for SSVEP features from QR code and checkerboard stimulus patterns (shown in Table 4 and Table 5).
Figure 7
Figure 7
Average classification accuracy of each steering command between checkerboard and QR patterns using relative PSD method (1: only fundamental flicker frequency and 2: mixing fundamental and first harmonic frequency).
Figure 8
Figure 8
(a) The routes for testing (distance: 20 m per route). (b) Example scenario of the experiment while participant uses the proposed BCI to control the simulated wheelchair.
Figure 9
Figure 9
Average times required by all participants to complete route 1.
Figure 10
Figure 10
Average times required by all participants to complete route 2.
See this image and copyright information in PMC

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