Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game

Abstract

BACKGROUND: This study focused on developing an upper limb rehabilitation program. In this regard, a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video was designed. OBJECTIVE: In particular, the synergetic effect of the game was investigated by combining the action observation paradigm with BCI based FES. METHODS: The BCI-FES system was contrasted under two conditions: with flickering action video and flickering noise video. In this regard, 11 right-handed subjects aged between 22–27 years were recruited. The differences in brain activation in response to the two conditions were examined. RESULTS: The results indicate that T3 and P3 channels exhibited greater Mu suppression in 8–13 Hz for the action video than the noise video. Furthermore, T4, C4, and P4 channels indicated augmented high beta (21–30 Hz) for the action in contrast to the noise video. Finally, T4 indicated suppressed low beta (14–20 Hz) for the action video in contrast to the noise video. CONCLUSION: The flickering action video based BCI-FES system induced a more synergetic effect on cortical activation than the flickering noise based system.

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions

“…The remaining (30) works tested their systems exclusively with healthy subjects. Nine of the identified studies tested the BCI system in more than ten able-bodied subjects ( Brunner et al, 2011 ; Horki et al, 2011 ; Sakurada et al, 2013 ; Kwak et al, 2015 ; Chen et al, 2018 ; Delijorge et al, 2020 ; Son et al, 2020 ; Zhu et al, 2020 ). Of the four studies that recruited both healthy subjects and patients ( Sakurada et al, 2013 ; Tidoni et al, 2017 ; Okahara et al, 2018 ; Delijorge et al, 2020 ), only one ( Sakurada et al, 2013 ) reported the classification accuracy for both patients (88.46%) and healthy subjects (81.1%).…”

“…Regarding EEG electrodes, SSVEP and P300 BCI systems used a minimum of two recording channels for SSVEP ( Li et al, 2018 ) and 1 for P300 ( Bhattacharyya et al, 2014 ), and it goes up to a maximum of 19 for SSVEP ( Son et al, 2020 ) and 32 for P300 BCIs ( Duvinage et al, 2012 ; Huang et al, 2019 ). They are placed predominantly over the parietal and occipital (visual cortex) regions, in the positions P3, Pz, P4, PO3, PO4, T5, T6, O1, Oz, and O2 of the 10–20 International system for EEG electrode placement.…”

“…They are (1) Orthosis ( Ortner et al, 2011 ; Duvinage et al, 2012 ; Stan et al, 2015 ; Delijorge et al, 2020 ) and exoskeleton ( Gui et al, 2015 ; Kwak et al, 2015 ; Bhagat et al, 2016 ), used to perform sequences of movements to activate the hand, wrist, arm, leg or foot. (2) FES, which has been reported to be of help to regain coordination and improve performance in functional tasks ( Do et al, 2011 ; Ding-Guo and Ying, 2012 ; Yao et al, 2012 ; McCabe et al, 2015 ; van Dokkum et al, 2015 ; Choi et al, 2016 ; Osuagwu et al, 2016 ; Zhao et al, 2016 ; Son et al, 2020 ). (3) Prosthesis ( Li et al, 2018 ), and (4) VR (VEP-based BCI systems immersed in virtual environment) ( Su et al, 2011 ; Koo et al, 2015 ; Tidoni et al, 2017 ; Touyama and Sakuda, 2017 ; Choi et al, 2019 ; Huang et al, 2019 ; Yao et al, 2019 ).…”

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

“…DSI-24 is a wireless EEG headset developed by the Wearable Sensing Company of the United States. DSI-24 has 21 signal-recording dry electrodes, including 19 electrodes on the head for full head coverage, 2 ear clip electrodes, and 3 auxiliary sensors [ 49 ]. The electrodes are spring-loaded to provide constant and comfortable pressure, which enhances the contact between electrodes and the skin and, at the same time, reduces motion artifacts.…”

Cortical activation of neuromuscular electrical stimulation synchronized mirror neuron rehabilitation strategies: an fNIRS study

“…An fNIRS-BCI based on brain activity induced by MNS rehabilitation strategies combined with NMES-induced haptic feedback is feasible (43). By transforming cortical activity into ES in the hand muscles, BCI helps to connect the neural system and muscles, thereby improving hand rehabilitation (44). MNS-based rehabilitation strategies combined with FES have applied this principle for the design and implementation of BCI, which could promote the activation of the MNS brain region (18).…”