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

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.

Applications of Brain Wave Classification for Controlling an Intelligent Wheelchair

“…Furthermore, it is important to state that the signals in dataset B had constraints in AF3 and AF4 electrodes, which may be important electrodes according to Lin and Lo [60] and Muñoz et al [61], thus decreasing the obtained accuracy and the F1-score. The average accuracy of other authors using the EPOC and the magnitude of frequency components or the power spectral density (square of the magnitude) as features was 74-100% for Abiyev et al [57], 70% for Hurtado-Rincon et al [59], and 86-92% for Lin and Lo [60], and Siribunyaphat and Punsawad [67]. More recent works [49,68,69] have also achieved important F1-scores, using different EEG headsets.…”

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm

“…The experimental design enabled the identification of the most effective set of algorithms, optimizing the accuracy and ITR of the BCI. Although previous studies have utilized PSD, FFT, and CCA algorithms for feature selection, as cited in references [ 1 , 3 , 4 , 7 , 10 , 24 , 25 , 26 , 27 , 30 ], an experimental design enables the selection of the most suitable method, taking into account relevant factors specific to the experiment, such as the type of EEG helmet, type of electrodes, and number of channels. Moreover, it is observed that the utilization of convolutional neural networks is highly frequent.…”

“…Prior studies have indicated that it is typical to consider users’ prior training. For instance, in [ 4 ], the training duration was 10 min, whereas in [ 1 ], it spanned many weeks. This extended training period contributes to the higher accuracies achieved.…”

“…The practical application of technology such as wheelchairs, exoskeletons, or robotic arms can be limited due to complications in control methods which can affect their efficacy and reliability. It is imperative to confront and overcome these obstacles to fully harness the capabilities of assistive robotic technologies [ 4 , 5 , 6 ].…”