A Systematic Review of Using Deep Learning Technology in the Steady‐State Visually Evoked Potential‐Based Brain‐Computer Interface Applications: Current …
The significance of deep learning techniques in relation to steady‐state visually evoked
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …
potential‐(SSVEP‐) based brain‐computer interface (BCI) applications is assessed through …
Deep learning-based construction equipment operators' mental fatigue classification using wearable EEG sensor data
Operator attention failure due to mental fatigue during extended equipment operations is a
common cause of equipment-related accidents that result in catastrophic injuries and …
common cause of equipment-related accidents that result in catastrophic injuries and …
EEG-based investigation of the impact of room size and window placement on cognitive performance
This study investigated changes in scalp electroencephalography (EEG) features associated
with short-term exposure to four virtual classroom designs, with different window placement …
with short-term exposure to four virtual classroom designs, with different window placement …
[HTML][HTML] Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning
Pathology diagnosis based on EEG signals and decoding brain activity holds immense
importance in understanding neurological disorders. With the advancement of artificial …
importance in understanding neurological disorders. With the advancement of artificial …
Investigating user proficiency of motor imagery for EEG-based BCI system to control simulated wheelchair
T Saichoo, P Boonbrahm, Y Punsawad - Sensors, 2022 - mdpi.com
The research on the electroencephalography (EEG)-based brain–computer interface (BCI)
is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency …
is widely utilized for wheelchair control. The ability of the user is one factor of BCI efficiency …
On-line learning, classification and interpretation of brain signals using 3D SNN and ESN
P Koprinkova-Hristova, D Penkov… - … Joint Conference on …, 2023 - ieeexplore.ieee.org
The paper proposes a novel hierarchical recurrent neural network architecture for on-line
classification and interpretation of EEG data. It incorporates two dynamic pools of neurons …
classification and interpretation of EEG data. It incorporates two dynamic pools of neurons …
EEG-based investigation of the impact of classroom design on cognitive performance of students
This study investigated the neural dynamics associated with short-term exposure to different
virtual classroom designs with different window placement and room dimension. Participants …
virtual classroom designs with different window placement and room dimension. Participants …
Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG)
Neurological disorders affecting speech production adversely impact quality of life for over 7
million individuals in the US. Traditional speech interfaces like eyetracking devices and …
million individuals in the US. Traditional speech interfaces like eyetracking devices and …
Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures
This work addresses the challenge of classifying multiclass visual EEG signals into 40
classes for brain–computer interface applications using deep learning architectures. The …
classes for brain–computer interface applications using deep learning architectures. The …
Epileptic seizure prediction and classification based on statistical features using LSTM fully connected neural network
Epilepsy is the most common neurological disorder by which over 65 million people are
affected across the world. Recent research has shown a very large interest to predict and …
affected across the world. Recent research has shown a very large interest to predict and …