Feb 26, 2025 · This study designed an ultra-high-density (UHD) EEG system to decode multi-directional arm movements. The system contains 200 electrodes with an interval of about 4 mm. We analyzed the patterns of the UHD EEG signals induced by arm movements in …

Aug 1, 2021 · In order to validate that arm movement intentions can be detected in advance for each arm and with a mobile EEG, an experiment was conducted. The experimental design aimed to collect and label the EEG data with the different …

Dec 14, 2016 · In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of...

EEG Controlled Robotic Arm Using Fuzzy Logic Controller Abstract: This paper presents an Electroencephalogram (EEG)-controlled robotic arm system designed to establish a direct interface between human brain signals and precise robotic arm movements.

Dec 22, 2023 · This study presents an EEG-based control method using AI for a robotic arm neurorehabilitation system. The system employs advanced EEG technology to capture and interpret brainwaves, utilizing AI algorithms for analysis and translation.

The project proposes an approach towards EEG-driven position control of a robot arm by utilizing motor imagery, P300 waveform and Visually evoked Potential to align the robot arm with desired target position. The user produces motor imagery signals to control the motion of the arm.

Aug 1, 2022 · In this paper, based on the idea of a hybrid BCI, a hybrid EEG and EMG signals control system for the robotic arm is proposed, which combines unilateral arm and left and right hand motor imagery.

Aug 11, 2020 · Continuous LF-EEG-based movement decoding for the online control of a robotic arm was achieved for the first time. The potential bottlenecks arising when switching from offline to online decoding, and possible solutions, were described.

Three key elements characterise the EEG-based prosthetic arm: the type of EEG signals, which part of the prosthetic arm is under control, and how to translate the EEG signal to a control command to manage the prosthesis.

In this project, we used machine learning techniques to detect hand movements, such as grasping and lifting, in EEG data. These hand movements can be used to control robotic prosthetic arms. We experimented with three data preprocessing techniques for EEG signals: butter low pass filtering, wavelet denoising, and stacking butter low pass filters.