A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface
F Mattioli, C Porcaro… - Journal of Neural …, 2022 - iopscience.iop.org
Objective. Brain-computer interface (BCI) aims to establish communication paths between
the brain processes and external devices. Different methods have been used to extract …
the brain processes and external devices. Different methods have been used to extract …
Noninvasive EEG-Based Intelligent Mobile Robots: A Systematic Review
Brain-controlled mobile robotics can provide restoration of mobility for individuals with
severe physical disabilities and empower healthy people with a broader reachable range in …
severe physical disabilities and empower healthy people with a broader reachable range in …
Eye-Gaze controlled wheelchair based on deep learning
J Xu, Z Huang, L Liu, X Li, K Wei - Sensors, 2023 - mdpi.com
In this paper, we design a technologically intelligent wheelchair with eye-movement control
for patients with ALS in a natural environment. The system consists of an electric wheelchair …
for patients with ALS in a natural environment. The system consists of an electric wheelchair …
A literature review on the smart wheelchair systems
Y Kim, B Velamala, Y Choi, Y Kim, H Kim… - arXiv preprint arXiv …, 2023 - arxiv.org
This study offers an in-depth analysis of smart wheelchair (SW) systems, charting their
progression from early developments to future innovations. It delves into various Brain …
progression from early developments to future innovations. It delves into various Brain …
Single trial detection of error-related potentials in brain–machine interfaces: a survey and comparison of methods
Objective. Error-related potential (ErrP) is a potential elicited in the brain when humans
perceive an error. ErrPs have been researched in a variety of contexts, such as to increase …
perceive an error. ErrPs have been researched in a variety of contexts, such as to increase …
EEG Motor Imagery Classification by Feature Extracted Deep 1D-CNN and Semi-Deep Fine-Tuning
M Taghizadeh, F Vaez, M Faezipour - IEEE Access, 2024 - ieeexplore.ieee.org
The main goal of this paper is to introduce a Motor Imagery (MI) classification system for
electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose …
electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose …
A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set
Human-in-the-loop approaches can greatly enhance the human–robot interaction by making
the user an active part of the control loop, who can provide a feedback to the robot in order …
the user an active part of the control loop, who can provide a feedback to the robot in order …
A deep neural network and transfer learning combined method for cross-task classification of error-related potentials
Background Error-related potentials (ErrPs) are electrophysiological responses that
naturally occur when humans perceive wrongdoing or encounter unexpected events. It …
naturally occur when humans perceive wrongdoing or encounter unexpected events. It …
EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification
W Wang, B Li, H Wang, X Wang, Y Qin, X Shi… - Medical & Biological …, 2024 - Springer
Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising
paradigm for brain-computer interface (BCI) systems and has been extensively employed in …
paradigm for brain-computer interface (BCI) systems and has been extensively employed in …
Stereo-RIVO: Stereo-Robust Indirect Visual Odometry
Mobile robots and autonomous systems rely on advanced guidance modules which often
incorporate cameras to enable key functionalities. These modules are equipped with visual …
incorporate cameras to enable key functionalities. These modules are equipped with visual …