Facial-Machine Interface-based Virtual Reality Wheelchair Control using EEG Artifacts of Emotiv Neuroheadset

Saichoo, Theerat; Boonbrahm, Poonpong; Punsawad, Yunyong

doi:10.1109/ecti-con51831.2021.9454926

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…In a similar vein, a 2021 study by researchers explored a facial-machine interface system based on EEG artifacts to enhance mobility in individuals with paraplegia using the Emotiv Neuroheadset. Results indicated that combining eye and jaw movements can be highly efficient, suggesting a practical hybrid BCI system for wheelchair control [15].…”

Section: State Of the Artmentioning

confidence: 99%

“…These devices capture a wide range of brain signals from the prefrontal area of the user's skull, specifically from points (Fp1) as observed in Figure 6, obtaining Delta (1-3 Hz), Theta (4-7 Hz), Alpha (8-13 Hz, subdivided into high and low), Beta (14-30 Hz, subdivided into high and low), and Gamma (31-100 Hz, subdivided into high and low) waves [14]. These devices feature a chip called TGAM integrated into the headbands, which performs essential preprocessing of the data, filtering noise in the extraction of these characteristics [15]. In addition to the brain signals, they provide attention and meditation indices calculated from the processed EEG signals.…”

Section: Eeg Data Acquisitionmentioning

confidence: 99%

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Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs

Rivas,

Sierra,

Cámara

2024

Electronics

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Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment.

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Section: State Of the Artmentioning

confidence: 99%

Section: Eeg Data Acquisitionmentioning

confidence: 99%