AIMC Topic: Brain-Computer Interfaces
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A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.
Sensors (Basel, Switzerland)
Dec 30, 2024
In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory...
EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.
Scientific reports
Dec 28, 2024
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effec...
Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.
Medical & biological engineering & computing
Dec 27, 2024
This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance...
Emotion recognition using multi-scale EEG features through graph convolutional attention network.
Neural networks : the official journal of the International Neural Network Society
Dec 27, 2024
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cogniti...
Decoding thoughts, encoding ethics: A narrative review of the BCI-AI revolution.
Brain research
Dec 22, 2024
OBJECTIVES: This narrative review aims to analyze mechanisms underlying Brain-Computer Interface (BCI) and Artificial Intelligence (AI) integration, evaluate recent advances in signal acquisition and processing techniques, and assess AI-enhanced neur...
Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.
Sensors (Basel, Switzerland)
Dec 19, 2024
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training...
Improving the Performance of Electrotactile Brain-Computer Interface Using Machine Learning Methods on Multi-Channel Features of Somatosensory Event-Related Potentials.
Sensors (Basel, Switzerland)
Dec 17, 2024
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contr...
Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.
Computers in biology and medicine
Dec 12, 2024
This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currentl...
Unlocking Security for Comprehensive Electroencephalogram-Based User Authentication Systems.
Sensors (Basel, Switzerland)
Dec 11, 2024
With recent significant advancements in artificial intelligence, the necessity for more reliable recognition systems has rapidly increased to safeguard individual assets. The use of brain signals for authentication has gained substantial interest wit...
MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.
IEEE journal of biomedical and health informatics
Dec 5, 2024
OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the c...
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