The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation using exoskeleton/exosuit. For system development, the conventional classificatio...
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stat...
Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless...
Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery and increasingly explored in brain-machine interface applications. Current devic...
IEEE journal of biomedical and health informatics
May 4, 2023
OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its applicati...
Medical & biological engineering & computing
Apr 25, 2023
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the applica...
Computer methods in biomechanics and biomedical engineering
Mar 23, 2023
Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in b...
Medical & biological engineering & computing
Feb 23, 2023
Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG s...
IEEE transactions on neural networks and learning systems
Feb 3, 2023
In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on...