Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review
Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices
through the utilization of brain waves. It is worth noting that the application of BCI is not …
through the utilization of brain waves. It is worth noting that the application of BCI is not …
Trends in EEG signal feature extraction applications
AK Singh, S Krishnan - Frontiers in Artificial Intelligence, 2023 - frontiersin.org
This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on
common feature extraction techniques mentioned in the research literature, as well as a …
common feature extraction techniques mentioned in the research literature, as well as a …
Motor-imagery-based brain–computer interface using signal derivation and aggregation functions
Brain–computer interface (BCI) technologies are popular methods of communication
between the human brain and external devices. One of the most popular approaches to BCI …
between the human brain and external devices. One of the most popular approaches to BCI …
EEG-based drowsiness detection with fuzzy independent phase-locking value representations using lagrangian-based deep neural networks
Passive electroencephalogram (EEG) brain–computer interfaces (BCI) have common usage
in the area of Driver Drowsiness Detection. The approach presented herein identifies the …
in the area of Driver Drowsiness Detection. The approach presented herein identifies the …
Driver drowsiness detection: An approach based on intelligent brain–computer interfaces
Estimating reaction times (RTs) and drowsiness states from brain signals is a notable step in
creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating …
creating passive brain–computer interfaces (BCIs). Prior to the deep learning era, estimating …
Virtual reality cognitive gaming based on brain computer interfacing: A narrative review
The present article explores the most popular approaches and the best practices for the
design and implementation of cognitive gaming interventions that combine Brain Computer …
design and implementation of cognitive gaming interventions that combine Brain Computer …
Electroencephalogram based reaction time prediction with differential phase synchrony representations using co-operative multi-task deep neural networks
Driver drowsiness is receiving a lot of deliberation as it is a major cause of traffic accidents.
This paper proposes a method which utilizes the fuzzy common spatial pattern optimized …
This paper proposes a method which utilizes the fuzzy common spatial pattern optimized …
Enhancing detection of SSVEP-based BCIs via a novel CCA-based method
X Yuan, Q Sun, L Zhang, H Wang - Biomedical Signal Processing and …, 2022 - Elsevier
Objective Frequency recognition methods based on spatial filtering have been widely
studied to enhance the classification performance of steady-state visual evoked potential …
studied to enhance the classification performance of steady-state visual evoked potential …
A novel EEG channel selection and classification methodology for multi‐class motor imagery‐based BCI system design
K Jindal, R Upadhyay, HS Singh - International Journal of …, 2022 - Wiley Online Library
Multi‐class MI EEG analysis is an extensively used paradigm in BCI. However, multiple EEG
channels lead to redundant information extraction and would reduce the distinction among …
channels lead to redundant information extraction and would reduce the distinction among …
Fuzzy divergence based analysis for eeg drowsiness detection brain computer interfaces
EEG signals can be processed and classified into commands for brain-computer interface
(BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to …
(BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to …