[HTML][HTML] An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface

X Geng, D Li, H Chen, P Yu, H Yan, M Yue - Alexandria Engineering …, 2022 - Elsevier
The electroencephalogram (EEG) signals based on the Brian-computer Interface (BCI)
equipment is weak, non-linear, non-stationary and time-varying, so an effective feature …

Imagined Speech Reconstruction from Neural Signals–An Overview of Sources and Methods

J Tang, J Chen, X Xu, A Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Imagined speech, also known as inner, covert, or silent speech, means how to express
thoughts silently without moving the vocal apparatus. Imagined speech reconstruction (ISR) …

EEG based direct speech BCI system using a fusion of SMRT and MFCC/LPCC features with ANN classifier

PP Mini, T Thomas, R Gopikakumari - Biomedical Signal Processing and …, 2021 - Elsevier
Brain computer interface (BCI) technology has a great deal of scientific interest with various
application systems. An advancement that is increasingly relevant in the BCI is …

Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural …

H Park, B Lee - Frontiers in human neuroscience, 2023 - frontiersin.org
Introduction In this study, we classified electroencephalography (EEG) data of imagined
speech using signal decomposition and multireceptive convolutional neural network. The …

AFLEMP: Attention-based Federated Learning for Emotion recognition using Multi-modal Physiological data

N Gahlan, D Sethia - Biomedical Signal Processing and Control, 2024 - Elsevier
Automated emotion recognition systems utilizing physiological signals are essential for
affective computing and intelligent interaction. Combining the multiple physiological signals …

EM-CSP: an efficient multiclass common spatial pattern feature method for speech imagery EEG signals recognition

D Alizadeh, H Omranpour - Biomedical Signal Processing and Control, 2023 - Elsevier
Background Brain-computer interface (BCI) technology has many applications in various
scientific fields, such as used in communication (speech recognition). The data of imagery …

Improving Silent Speech BCI Training Procedures Through Transfer from Overt to Silent Speech

M Rekrut, AM Selim, A Krüger - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Silent speech Brain-Computer Interfaces (BCIs) try to decode imagined speech from brain
activity. Those BCIs require a tremendous amount of training data usually collected during …

Acoustic emission and electromagnetic radiation precursor signal identification and early warning of coal and gas outburst based on diffusion-semi-supervised …

B Liu, Z Li, Z Zang, E Wang, C Zhang, S Yin - Physics of Fluids, 2024 - pubs.aip.org
Gas outbursts in coal seams represent a severe and formidable hazard, posing a significant
threat to the safety of coal mining operations. The advanced early warning is a crucial …

Non-Intrusive Load Identification Considering Unknown Load Based on Bi-Modal Fusion and One-Class Classification

J Xiao, M Tan, R Pan, Y Su, T Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Nonintrusive load monitoring (NILM) enables the real-time monitoring and data analysis of
energy consumption, providing more accurate, efficient, and intelligent data support for …

Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures

R Juyal, H Muthusamy, N Kumar, A Tiwari - Physical and Engineering …, 2024 - Springer
Communication is challenging for disabled individuals, but with advancement of brain-
computer interface (BCI) systems, alternative communication systems can be developed …