AIMC Topic: Brain-Computer Interfaces

Clear Filters Showing 1 to 10 of 643 articles

Advancing BCI with a transformer-based model for motor imagery classification.

Scientific reports Jul 2, 2025
Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI)...
Humans Neural Networks, Computer Electroencephalography Brain-Computer Interfaces Imagination Machine Learning Algorithms Deep Learning
View on PubMed DOI

EEG based real time classification of consecutive two eye blinks for brain computer interface applications.

Scientific reports Jul 1, 2025
Human eye blinks are considered a significant contaminant or artifact in electroencephalogram (EEG), which impacts EEG-based medical or scientific applications. However, eye blink detection can instead be transformed into a potential application of b...
Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Male Support Vector Machine Blinking Machine Learning Brain Female Young Adult Adult Humans Neural Networks, Computer
View on PubMed DOI

Improving EEG based brain computer interface emotion detection with EKO ALSTM model.

Scientific reports Jul 1, 2025
Decoding signals from the CNS brain activity is done by a computer-based communication device called a BCI. In contrast, the system is considered compelling communication equipment enabling command, communication, and action without using neuromuscul...
Brain Female Adult Humans Algorithms Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Male Emotions
View on PubMed DOI

Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.

Scientific reports Jul 1, 2025
The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement inte...
Imagination Brain-Computer Interfaces Movement Humans Neural Networks, Computer Algorithms Brain Electroencephalography Signal Processing, Computer-Assisted
View on PubMed DOI

Revisiting Euclidean alignment for transfer learning in EEG-based brain-computer interfaces.

Journal of neural engineering Jun 30, 2025
Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is tim...
Transfer, Psychology Electroencephalography Brain-Computer Interfaces Humans Machine Learning Algorithms
View on PubMed DOI

Investigating the benefits of artificial neural networks over linear approaches to BMI decoding.

Journal of neural engineering Jun 27, 2025
Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studi...
Neural Networks, Computer Movement Motor Cortex Macaca mulatta Linear Models Brain-Computer Interfaces Fingers Animals Male
View on PubMed DOI

Speech imagery brain-computer interfaces: a systematic literature review.

Journal of neural engineering Jun 26, 2025
Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whos...
Speech Electroencephalography Brain-Computer Interfaces Humans Imagination Brain
View on PubMed DOI

Learning from small datasets-review of workshop 6 of the 10th International BCI Meeting 2023.

Journal of neural engineering Jun 24, 2025
In a brain-computer interface (BCI), a primary objective is to reduce calibration time by recording as few as possible novel data points to (re-)train decoder models.Minimizing the calibration can be crucial for enhancing the usability of a BCI appli...
Deep Learning Electroencephalography Datasets as Topic Humans Education Machine Learning Brain-Computer Interfaces Congresses as Topic
View on PubMed DOI

POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

Journal of neural engineering Jun 11, 2025
. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal wa...
Electroencephalography Humans Machine Learning Brain-Computer Interfaces Imagination Neural Networks, Computer Adult Male
View on PubMed DOI

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.

Sensors (Basel, Switzerland) May 18, 2025
Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to promin...
Brain Electroencephalography Signal Processing, Computer-Assisted Machine Learning Algorithms Brain-Computer Interfaces Humans Neural Networks, Computer
View on PubMed DOI
Popular Topics
Recent Journals